recode hive
+recode hive
-
+
+
## βοΈ License
@@ -254,12 +254,11 @@ Stay up to date with the latest from recode hive:
---
-
[](#contributors)
[](https://github.com/recodehive/recode-website/stargazers)
@@ -16,7 +16,7 @@
[](https://github.com/recodehive/recode-website/graphs/contributors)
[](https://github.com/recodehive/recode-website/LICENSE)
-
+
@@ -235,7 +235,7 @@ Join our community and connect with fellow learners:
We appreciate all contributions to recode hive! Thank you to everyone who has helped make this project better.
- Collaboration 1st , code 2nd.
+Collaboration 1st , code 2nd.
**Your all-in-one resource for learning Git, GitHub, Python through comprehensive tutorials and hands-on projects.** @@ -199,7 +199,7 @@ git push origin feature/your-feature-nameHow to Contribute to this Repo | How to Setup - Watch Video
-
+
-
+
**Happy open-source contributionsβhere's to your career success! π**
-.default})
+
## Build Your First Pipeline: Step by Step
@@ -267,7 +267,7 @@ Your pipeline now runs automatically every night at 2am, copying new sales data
Let's step back and look at what you built:
-.default})
+
This is the **Extract and Load** part of ETL. The file is extracted from the source container and loaded into the bronze layer, untouched, exactly as it arrived.
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@@ -0,0 +1,465 @@
+---
+title: "Azure Data Pipeline Cost Optimization: How We Cut a $4,200 Bill by 73%"
+authors: [Aditya-Singh-Rathore]
+sidebar_label: "Azure Data Pipeline Cost Optimization"
+tags: [azure, cost-optimization, data-pipeline, adls-gen2, azure-synapse, apache-spark, azure-data-factory, delta-lake, data-engineering, cloud-cost]
+date: 2026-05-15
+
+description: Six costly mistakes in a 2GB Azure data pipeline drove a $4,200 monthly bill. Here's every mistake, root cause, and fix β with before-and-after numbers for each.
+
+draft: false
+canonical_url: https://www.recodehive.com/blog/azure-cost-optimization-data-pipelines
+
+meta:
+ - name: "robots"
+ content: "index, follow"
+ - property: "og:title"
+ content: "Azure Data Pipeline Cost Optimization: How We Cut a $4,200 Bill by 73%"
+ - property: "og:description"
+ content: "Six costly mistakes in a 2GB Azure data pipeline drove a $4,200 monthly bill. Here's every mistake, root cause, and fix β with before-and-after numbers."
+ - property: "og:type"
+ content: "article"
+ - property: "og:url"
+ content: "https://www.recodehive.com/blog/azure-cost-optimization-data-pipelines"
+ - property: "og:image"
+ content: "./images/cover.png"
+ - name: "twitter:card"
+ content: "summary_large_image"
+ - name: "twitter:title"
+ content: "Azure Data Pipeline Cost Optimization: How We Cut a $4,200 Bill by 73%"
+ - name: "twitter:description"
+ content: "Six costly mistakes, one pipeline, 2GB of data, a $4,200 bill. Here's how we fixed all of them."
+ - name: "twitter:image"
+ content: "./images/cover.png"
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+
+
+# Azure Data Pipeline Cost Optimization: How We Cut a $4,200 Bill by 73%
+
+The Azure billing email arrived on the first of the month. **$4,247.83.**
+
+Our pipeline processed roughly 2GB of sales data per day and served a Power BI dashboard to 30 users. There was no logical reason for a bill that size. Over the next three days, a line-by-line audit of Azure Cost Analysis revealed not one big mistake but **six medium-sized ones**, each silently running up costs in parallel, invisible until the invoice arrived.
+
+This post is that investigation: every mistake explained, every fix documented, and the exact before-and-after numbers. If you're building data pipelines on Azure and haven't audited your costs recently, at least one of these is probably happening to you right now.
+
+**What you'll learn in this post:**
+- Why a Dedicated SQL Pool running 24/7 is the single most expensive default mistake in Azure Synapse
+- How to replace nightly full loads with watermark-based incremental loads in Azure Data Factory
+- How to right-size Spark pools and configure auto-termination to stop paying for idle compute
+- How partition pruning on Delta Lake tables can reduce data scanned by over 90%
+- How ADLS Gen2 lifecycle policies passively save money on storage with zero ongoing effort
+- When a scheduled micro-batch replaces a 24/7 streaming pipeline without any business impact
+
+
+
+## The Pipeline Architecture
+
+Before the mistakes make sense, here is the full pipeline:
+
+```
+Daily sales data from REST API (~2GB/day)
+ β
+ADF Pipeline (ingestion)
+ β
+ADLS Gen2 β bronze/ (raw Parquet files)
+ β
+Spark job (transformation)
+ β
+ADLS Gen2 β silver/ (Delta tables)
+ β
+Dedicated SQL Pool (serving layer)
+ β
+Power BI dashboard (30 users)
+```
+
+A standard [Medallion Architecture](https://www.recodehive.com/blog/medallion-architecture), nothing exotic. 2GB of data per day. 30 users. Should have cost a few hundred dollars a month at most. It cost **~$4,247.83**. Here is exactly why.
+
+
+## Mistake #1: Dedicated SQL Pool Running 24/7
+
+**Monthly cost of this mistake: ~$1,800**
+
+This was the single largest line item. A Dedicated SQL Pool at DW200c was provisioned to serve the Power BI dashboard and left running continuously 24 hours a day, 7 days a week because auto-pause had never been configured.
+
+The thing that surprised me most when I first dug into the bill was this: the SQL Pool charged us the same rate at 3am on a Saturday as it did during peak usage on a Tuesday afternoon. I had assumed, naively, that there was some kind of idle detection built in. There isn't. When it's provisioned, you're paying - full stop, whether a single query runs or not. Our 30 users were active between 9am and 6pm on weekdays, 45 hours of actual usage per week. The pool was running for 168 hours per week. That's 123 hours of idle, fully-billed compute every single week, and it showed up on our invoice as a flat $1,800 charge with no breakdown by usage.
+
+:::note
+The SQL Pool doesn't throttle billing when it's idle, provisioned capacity is billed by the hour regardless of query activity. Pausing the pool is the only way to stop the DWU clock. Storage costs continue when paused, but the compute component which is the large part, stops completely.
+:::
+
+### The Fix: Auto-Pause with Azure Automation Runbooks
+
+The solution is two Azure Automation runbooks, one to pause the pool at the end of business hours, one to resume it in the morning. The runbooks use Managed Identity for authentication, which avoids hardcoding credentials.
+
+```python title="pause-sql-pool.py"
+# Azure Automation Runbook β pause Synapse SQL Pool outside business hours
+from azure.identity import ManagedIdentityCredential
+from azure.mgmt.synapse import SynapseManagementClient
+
+credential = ManagedIdentityCredential()
+client = SynapseManagementClient(credential, subscription_id)
+
+# Pause at 7pm weekdays
+client.sql_pools.begin_pause(
+ resource_group_name,
+ workspace_name,
+ sql_pool_name
+)
+```
+
+Schedule the pause runbook at 7pm weekdays and the resume runbook at 8am. Weekends stay paused unless a manual override is triggered through the Azure portal.
+
+**Result:** Billed hours dropped from 720 to roughly 210 per month. SQL Pool cost fell from ~$1,800/month to ~$530/month, a saving of **$1,270/month**.
+
+:::tip
+If your workload is exploratory rather than dashboard-serving, consider whether [Serverless SQL Pool](https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/on-demand-workspace-overview) is sufficient. Serverless pools bill per TB of data scanned rather than provisioned DWUs, which can be significantly cheaper for infrequent query patterns.
+:::
+
+
+## Mistake #2: Full Load Running Every Night Instead of Incremental
+
+**Monthly cost of this mistake: ~$620**
+
+The ADF pipeline was configured to pull **all records** from the source database on every nightly run not just new or updated ones. Day 1 it pulled 2GB. By Day 60 it was pulling 120GB, processing records that had already been processed 59 times before.
+
+This pattern is extremely common and extremely expensive. Every night, the ADF pipeline read the entire historical dataset, the Spark transformation job processed all of it, and the results were written back to Delta. The billable compute scaled with the dataset size, not with the actual volume of new data.
+
+### The Fix: Watermark-Based Incremental Loading
+
+A watermark stores the timestamp of the last successfully processed record. Every pipeline run reads only records newer than that timestamp, then updates the watermark on success.
+
+The implementation in ADF uses two Lookup activities and a query parameterized by the watermark value:
+
+
+
+
+```sql title="get-watermark.sql"
+-- Step 1: ADF Lookup activity β retrieve the last watermark
+SELECT last_processed_date
+FROM pipeline_watermarks
+WHERE pipeline_name = 'sales_ingestion'
+```
+
+```sql title="incremental-source-query.sql"
+-- Step 2: Source query, parameterized by watermark from Step 1
+SELECT *
+FROM orders
+WHERE updated_at > '@{activity("GetWatermark").output.firstRow.last_processed_date}'
+ AND updated_at <= '@{utcnow()}'
+```
+
+```sql title="update-watermark.sql"
+-- Step 3: After successful pipeline run, advance the watermark
+UPDATE pipeline_watermarks
+SET last_processed_date = '@{utcnow()}'
+WHERE pipeline_name = 'sales_ingestion'
+```
+
+
+
+
+| Pipeline Run | Records Processed | Data Volume | ADF Cost |
+|---|---|---|---|
+| Before (full load, Day 60) | ~4.8M rows | ~120 GB | ~$22/run |
+| After (incremental) | ~8,000 rows | ~2 GB | ~$0.40/run |
+
+
+
+
+**Result:** ADF activity runtime dropped by 94%. Spark compute for the transformation step fell proportionally. Combined saving: **~$585/month**.
+
+
+
+:::tip
+The watermark pattern requires a reliable `updated_at` or `created_at` column in the source table. If your source does not have one, work with the source team to add it, the cost saving on the pipeline side will far outweigh the schema migration effort.
+:::
+
+
+## Mistake #3: Spark Cluster Over-Provisioned for the Actual Workload
+
+**Monthly cost of this mistake: ~$480**
+
+When setting up the Spark pool in Azure Synapse, the default node size **DS3_v2 (4 cores, 14GB RAM)** was selected with 5 nodes. The actual workload: transforming 2β5GB of Parquet files daily with deduplication, type casting, and a few joins.
+
+Two problems compounded each other. First, the cluster was consuming roughly 10x the compute it actually needed for the data volume. Second, auto-termination was set to 60 minutes, meaning after a 12-minute job, the cluster sat idle and fully billed for another 48 minutes before shutting down.
+
+### The Fix: Right-Sizing, Autoscale, and Fast Termination
+
+The fix has three components that work together:
+
+```json title="synapse-spark-pool-config.json"
+{
+ "nodeSize": "Small",
+ "minNodeCount": 2,
+ "maxNodeCount": 4,
+ "autoscaleEnabled": true,
+ "autoTerminationEnabled": true,
+ "autoTerminationDelayInMinutes": 5
+}
+```
+
+The third component is tuning the shuffle partition count inside the Spark notebook. The default of 200 partitions is calibrated for large clusters and large datasets. For 2β5GB of data on a small cluster, 200 partitions creates unnecessary overhead that extends job runtime and therefore billed compute time.
+
+```python title="spark-notebook-config.py"
+# Place in the first cell of every Spark notebook
+# Default is 200 partitions β designed for multi-TB workloads
+# For 2β5 GB datasets, 8 is appropriate
+spark.conf.set("spark.sql.shuffle.partitions", "8")
+```
+
+:::info
+A good rule of thumb for `spark.sql.shuffle.partitions`: aim for roughly **128MB of data per partition**. For a 2GB dataset, that's approximately 16 partitions. Err slightly lower rather than higher for small datasets on small clusters.
+:::
+
+**Result:** Spark compute cost dropped from ~$580/month to ~$100/month, a saving of **$480/month**.
+
+
+## Mistake #4: Reading ADLS Gen2 Files Without Partition Pruning
+
+**Monthly cost of this mistake: ~$290**
+
+The Silver layer Delta table was partitioned by `order_date`. The Spark transformation job, however, was reading the entire table and applying a date filter *after* the read not during it.
+
+Think of it like this: imagine your filing cabinet is organized by month, one drawer per month, clearly labelled. Partition pruning is pulling open only January's drawer. What we were doing instead was dumping every drawer onto the floor, sifting through three years of paper, and throwing away everything that wasn't from January then tidying it all back up. Every. Single. Night. The cabinet is organized correctly. We just weren't using the labels.
+
+With 90 days of accumulated history, this approach was scanning 90x more data than necessary on every run. The fix is to push the filter into the read itself so Spark can use the partition directory structure to skip everything irrelevant before a single file is opened.
+
+
+
+
+```python title="transformation-before.py"
+# Reads the ENTIRE Delta table, then filters in memory
+# With 90 days of history: scans ~180GB to get ~2GB of useful data
+silver_df = spark.read.format("delta").load(
+ "abfss://data@mylake.dfs.core.windows.net/silver/sales/"
+)
+filtered_df = silver_df.filter(col("order_date") == yesterday)
+```
+
+
+
+
+```python title="transformation-after.py"
+from datetime import datetime, timedelta
+
+yesterday = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
+
+# Filter pushed into the read β Spark only opens yesterday's partition
+# With 90 days of history: scans ~2GB instead of ~180GB
+silver_df = (
+ spark.read.format("delta")
+ .load("abfss://data@mylake.dfs.core.windows.net/silver/sales/")
+ .filter(col("order_date") == yesterday)
+)
+```
+
+
+
+
+**Result:** Data scanned per run dropped from ~180GB to ~2GB. Spark runtime fell from 18 minutes to 4 minutes. Monthly saving: **~$260/month**.
+
+:::note
+For partition pruning to work, two conditions must both be true. The table must be partitioned by the filter column, and the filter must be applied at read time not in a subsequent transformation step. Applying the filter even one `.filter()` call after the `.load()` still results in a full table scan in some execution contexts.
+:::
+
+
+## Mistake #5: Keeping Historical Data on Hot Storage Tier
+
+**Monthly cost of this mistake: ~$180**
+
+The ADLS Gen2 Bronze layer had 14 months of raw Parquet files sitting on the **Hot** storage tier. No lifecycle policy had ever been configured.
+
+ADLS Gen2 charges different rates depending on the storage access tier. When I pulled our actual invoice line items, the numbers told a clear story: our Bronze container was costing us $0.023 per GB per month on Hot, while data we hadn't touched in months was sitting right next to yesterday's files paying the same rate. Moving files older than 30 days to Cool dropped that rate to roughly $0.013/GB, about 44% less for data we only needed occasionally. Files older than 180 days dropped to Archive at around $0.002/GB, which is where old Bronze raw files belong when the Silver layer already has the clean version.
+
+Fourteen months of ~2GB/day accumulates to roughly 850GB in the Bronze layer. The fix required exactly one policy configuration.
+
+### The Fix: ADLS Gen2 Lifecycle Management Policy
+
+```json title="lifecycle-policy.json"
+{
+ "rules": [
+ {
+ "name": "bronze-tier-management",
+ "type": "Lifecycle",
+ "definition": {
+ "filters": {
+ "prefixMatch": ["bronze/"],
+ "blobTypes": ["blockBlob"]
+ },
+ "actions": {
+ "baseBlob": {
+ "tierToCool": {
+ "daysAfterModificationGreaterThan": 30
+ },
+ "tierToArchive": {
+ "daysAfterModificationGreaterThan": 180
+ }
+ }
+ }
+ }
+ }
+ ]
+}
+```
+
+Bronze files automatically move to Cool after 30 days and Archive after 180 days β with no pipeline changes and no ongoing maintenance.
+
+
+
+:::tip
+Apply lifecycle policies to the Silver and Gold layers too, with longer thresholds. Silver data accessed for backfills after 90 days can move to Cool. Gold data older than 365 days can move to Archive if your reporting doesn't require historical drill-downs that old.
+:::
+
+**Result:** ~$160/month in combined storage and egress savings, purely passive, set once and forgotten.
+
+
+## Mistake #6: A Streaming Pipeline for 15-Minute Update Requirements
+
+**Monthly cost of this mistake: ~$380**
+
+A secondary pipeline fed a "near real-time" inventory dashboard. The product team had asked for updates *as fast as possible*, which was interpreted as: build a Kafka + Flink streaming pipeline with always-on infrastructure.
+
+What the product team actually needed, when pinned down to a specific number: inventory counts updated **every 15 minutes**.
+
+A streaming pipeline running 24/7 to deliver 15-minute updates is the cloud equivalent of leaving your car engine running all night because you have an early meeting. The always-on Kafka cluster and Flink job cost $380/month. The business requirement was achievable with a job that runs for 2β3 minutes, 96 times a day.
+
+### The Fix: Micro-Batch with ADF Tumbling Window Trigger
+
+An ADF Tumbling Window trigger fires the pipeline every 15 minutes. Each run reads only the delta since the last watermark, processes it, and shuts down. No infrastructure stays running between executions.
+
+```json title="tumbling-window-trigger.json"
+{
+ "type": "TumblingWindowTrigger",
+ "typeProperties": {
+ "frequency": "Minute",
+ "interval": 15,
+ "startTime": "2024-01-01T00:00:00Z",
+ "retryPolicy": {
+ "count": 3,
+ "intervalInSeconds": 30
+ }
+ }
+}
+```
+
+The pipeline runs for 2β3 minutes every 15 minutes, processes the delta since the last run using the same watermark pattern from Mistake #2, then shuts down. The product team's dashboard still updates every 15 minutes. They noticed zero difference.
+
+:::info
+A useful mental model for this decision: **streaming is the right choice when latency requirements are below 60 seconds**. For anything above that threshold, a well-designed micro-batch pipeline is almost always cheaper, simpler, easier to monitor, and easier to debug. The [hidden costs of streaming pipelines](https://www.recodehive.com/blog/batch-vs-stream-processing) go beyond compute they include more complex failure handling, harder-to-test logic, and longer debugging cycles.
+:::
+
+**Result:** Streaming infrastructure cost of $380/month replaced by ~$40/month in ADF + Spark compute. **$340/month saved.**
+
+
+
+
+## Before and After Summary
+
+:::info
+
+| Mistake | Monthly Cost Before | Monthly Cost After | Saving |
+|---|---|---|---|
+| Dedicated SQL Pool running 24/7 | $1,800 | $530 | **$1,270** |
+| Full load instead of incremental | $620 | $35 | **$585** |
+| Over-provisioned Spark cluster | $580 | $100 | **$480** |
+| No partition pruning | $290 | $30 | **$260** |
+| Hot storage for historical data | $180 | $20 | **$160** |
+| Streaming for 15-min updates | $380 | $40 | **$340** |
+| **Total** | **$3,850** | **$755** | **$3,095** |
+
+:::
+
+From $4,247 down to approximately $1,150 after all fixes, a **73% cost reduction** on a pipeline doing exactly the same work on exactly the same data.
+
+
+
+
+## Cost Optimization Checklist
+
+Run through this every quarter. Each item is a question, if the answer is "no" or "I don't know," investigate it.
+
+**Dedicated SQL Pool**
+- [ ] Is auto-pause configured for outside business hours?
+- [ ] Is Dedicated SQL Pool actually required, or would Serverless SQL Pool suffice for the query pattern?
+
+**ADF Pipelines**
+- [ ] Are any pipelines running full loads where incremental loads are possible?
+- [ ] Is a watermark implemented for every pipeline reading time-series data?
+
+**Spark Pools**
+- [ ] Is node size right-sized for actual data volume, not default?
+- [ ] Is auto-termination set to 5 minutes, not 30β60?
+- [ ] Is `spark.sql.shuffle.partitions` tuned to actual data size?
+- [ ] Is autoscale enabled with realistic min/max node counts?
+
+**ADLS Gen2**
+- [ ] Are lifecycle policies configured on all containers?
+- [ ] Are Delta tables partitioned by the column filtered most frequently?
+- [ ] Is partition pruning applied at read time in all Spark notebooks?
+
+**Streaming Infrastructure**
+- [ ] What is the actual latency requirement, in minutes?
+- [ ] If it is above 5 minutes β is a micro-batch pipeline in use instead of always-on streaming?
+
+
+## Key Lessons
+
+**Defaults are expensive by design.** Azure's defaults - 60-minute Spark termination, no SQL Pool auto-pause, no lifecycle policies are chosen for zero-friction setup, not cost efficiency. Every default should be reviewed and overridden deliberately on day one, not after the first billing surprise.
+
+**Incremental loading is not a future optimization.** For any pipeline reading time-series data from a growing source, a full load that runs daily compounds in cost the same way interest compounds on debt. The watermark pattern takes a few hours to implement and pays for itself within a week.
+
+**Partition pruning is free performance.** Setting up partitioning correctly at table creation and pushing filters into the read step costs nothing to implement and can reduce Spark compute by over 90%. The only requirement is knowing which column you filter on most frequently which you almost certainly already know.
+
+**"Real-time" almost never means real-time.** The product team's requirement was 15-minute updates. The engineering interpretation was 24/7 streaming infrastructure. The gap between those two decisions cost $340/month and made the pipeline significantly harder to maintain. Before designing streaming, ask for a specific latency number, then design to that number.
+
+**Azure Cost Analysis is a weekly habit, not a monthly emergency.** The six mistakes above were invisible until the invoice arrived. Fifteen minutes a week in [Azure Cost Management](https://learn.microsoft.com/en-us/azure/cost-management-billing/) catches problems while they are a $50 anomaly, not a $500 line item.
+
+
+## Frequently Asked Questions
+
+**Q: Should I always use Serverless SQL Pool instead of Dedicated SQL Pool to save costs?**
+A: We actually tested this switch on our own setup before committing to the auto-pause approach. Serverless made sense for us until we crossed about 6 hours of daily query time, below that threshold, serverless was cheaper every single month without exception, and we didn't need to manage any pause/resume scheduling at all. If your Power BI dashboard is hit heavily throughout the business day, dedicated will eventually win on pure cost. But if usage is bursty or confined to a few hours, don't provision dedicated capacity and then fight to keep it paused, just go serverless from the start.
+
+**Q: What if my source system doesn't have a reliable `updated_at` column for watermarking?**
+A: We ran into this with one of our source databases, no timestamp, no audit column, nothing. We ended up going the CDC route using Debezium, which captures row-level changes at the database log level without touching the source schema at all. It took about a day to set up and was the cleanest solution we found. For append-only tables, an auto-incrementing primary key works as a watermark substitute. If neither option exists, row hash comparison is a last resort, it detects changes, but you're still reading the full source every run, which defeats most of the point.
+
+**Q: How do I choose the right partition column for a Delta Lake table?**
+A: The short answer is: whichever column appears in your most common filter is your partition column, and for time-series data that's almost always a date. What I'd caution against is partitioning on something high-cardinality like user ID or transaction ID, we made that mistake early on and ended up with thousands of tiny files that made partition pruning useless and actually slowed down reads. A healthy partition should hold somewhere between 100MB and 1GB of data. If a partition is smaller than that, you're creating file overhead without the pruning benefit.
+
+**Q: Will moving data to Archive tier in ADLS Gen2 break my backfill pipelines?**
+A: I learned this the hard way, a backfill job failed at 2am because Archive data doesn't just open like a normal file. You have to explicitly rehydrate it first, and depending on the priority tier you choose, that can take anywhere from an hour to fifteen hours. We got caught by this once, and now the rule on our team is: only archive data where we have at least 24 hours of lead time if a backfill request comes in. For Bronze raw files older than 180 days, that's usually fine, nobody's doing emergency backfills on six-month-old raw data. For Silver and Gold, we stop at Cool tier and don't go to Archive at all, because the rehydration wait is unacceptable mid-incident.
+
+**Q: Is a 5-minute Spark auto-termination window too aggressive for interactive notebooks?**
+A: For scheduled production jobs, 5 minutes is ideal, the job finishes, and within 5 minutes the cluster is gone and billing stops. For interactive development work, 5 minutes will drive you crazy because the cluster spins down between notebook cells if you pause to think. What we do is maintain two separate Spark pool configurations: one for production jobs set to 5-minute termination, and one for dev work set to 60 minutes. They run on different node sizes too. Keep them separate and you get cost efficiency in production without interrupting development flow.
+
+**Q: How do I detect whether partition pruning is actually working in my Spark job?**
+A: The fastest way is to run `df.explain()` and look at the physical plan output. If partition pruning is active, you'll see a `PartitionFilters` entry in the scan node listing your filter predicate. If that field shows `PartitionFilters: []` - empty brackets, you're scanning the full table regardless of what your code looks like. I've caught this bug three times by checking the plan after what looked like a correctly written filter, and each time it turned out the filter was being applied one transformation step too late, after Spark had already committed to a full read.
+
+---
+
+## References and Further Reading
+
+- [Microsoft Docs - Azure Cost Management and Billing](https://learn.microsoft.com/en-us/azure/cost-management-billing/)
+- [Microsoft Docs - Synapse SQL Pool Pause and Resume](https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/pause-and-resume-compute-portal)
+- [Microsoft Docs - ADLS Gen2 Lifecycle Management Policies](https://learn.microsoft.com/en-us/azure/storage/blobs/lifecycle-management-overview)
+- [Delta Lake - Partition Pruning and Optimization](https://docs.delta.io/latest/optimizations-oss.html)
+- [Microsoft Docs - ADF Tumbling Window Trigger](https://learn.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger)
+- [RecodeHive - Azure Storage and ADLS Gen2 Complete Guide](https://www.recodehive.com/blog/azure-storage-options)
+- [RecodeHive - Hidden Cost of Streaming Pipelines](https://www.recodehive.com/blog/batch-vs-stream-processing)
+- [RecodeHive - Medallion Architecture Explained](https://www.recodehive.com/blog/medallion-architecture)
+
+
+
+## About the Author
+
+**Aditya Singh Rathore** is a Data Engineer focused on building modern, scalable data platforms on Azure. He writes about data engineering, cloud architecture, and real-world pipelines on [RecodeHive](https://www.recodehive.com/) β turning hard-won production lessons into content anyone can apply.
+
+π [LinkedIn](https://www.linkedin.com/in/aditya-singh-rathore0017/) | [GitHub](https://github.com/Adez017)
+
+π© Got an Azure bill that surprised you? Drop the line item in the comments β happy to help debug it.
+
+
+
+
+```python title="transformation-before.py"
+# Default: 200 shuffle partitions
+# On 8GB of data, each partition is ~40MB
+# 200 tasks scheduled, most doing trivial work
+# Task launch overhead dominates actual compute time
+
+df = (
+ spark.read.format("delta")
+ .load("abfss://data@lake.dfs.core.windows.net/bronze/sales/")
+ .groupBy("product_id", "event_date")
+ .agg(sum("revenue").alias("total_revenue"))
+)
+```
+
+
+
+
+```python title="transformation-after.py"
+# Set BEFORE any transformations run
+# Rule of thumb: Total shuffle data size Γ· 128MB
+# For ~8GB post-shuffle data: 8000 Γ· 128 β 64
+
+spark.conf.set("spark.sql.shuffle.partitions", "64")
+
+df = (
+ spark.read.format("delta")
+ .load("abfss://data@lake.dfs.core.windows.net/bronze/sales/")
+ .groupBy("product_id", "event_date")
+ .agg(sum("revenue").alias("total_revenue"))
+)
+```
+
+
+
+
+| Setting | Shuffle Partitions | Stage Duration | Tasks Launched |
+|---|---|---|---|
+| Default (200) | 200 | 48 min | 200 |
+| Tuned (64) | 64 | 11 min | 64 |
+
+
+
+
+**Result:** Aggregation stage dropped from 48 minutes to 11 minutes. **~37 minutes saved.**
+
+:::tip
+For Databricks on Delta Lake, you can also enable **Adaptive Query Execution (AQE)**, which automatically adjusts shuffle partitions at runtime based on actual data size:
+
+```python
+spark.conf.set("spark.sql.adaptive.enabled", "true")
+spark.conf.set("spark.sql.adaptive.coalescePartitions.enabled", "true")
+```
+
+AQE doesn't replace manual tuning but it acts as a safety net when your estimate is off. We run both: manual tuning as the primary setting, AQE as the fallback.
+:::
+
+
+## Mistake #2: Caching Everything (Or Nothing)
+
+**Time lost to this mistake: ~28 minutes**
+
+Caching is one of the most misunderstood features in PySpark. Beginners either avoid it entirely (paying to recompute the same DataFrame multiple times) or cache everything (consuming all available memory and forcing everything else to spill to disk).
+
+### What Caching Actually Does
+
+Calling `.cache()` on a DataFrame doesn't immediately store anything, Spark is lazy, so nothing happens until an action triggers computation. What `.cache()` actually does is plant a flag that says: *the first time you compute this, hold onto the result.* The next time something references this DataFrame, Spark reads from that stored result instead of re-running the entire computation from scratch.
+
+The reason this matters is that Spark has no implicit memory of previous computations. Without caching, every action that references `base_df` starts from the beginning, re-reading the source files, re-running the joins, re-applying the filters. We discovered this the painful way when a pipeline that looked like one job was actually running the most expensive stage twice, adding 28 minutes to every run.
+
+This only helps if you reference the same DataFrame more than once. If you compute a DataFrame, transform it once, and write it, caching adds overhead with zero benefit.
+
+
+
+```python
+# This caching is useless β df is only used once
+df = spark.read.format("delta").load(silver_path).cache()
+df.write.format("delta").save(gold_path)
+```
+
+The right time to cache is when a DataFrame is expensive to compute *and* you reference it in multiple downstream operations.
+
+
+
+
+```python title="pipeline-no-cache.py"
+# base_df is computed TWICE β once for each write
+# Spark re-reads and re-joins from scratch each time
+
+base_df = (
+ spark.read.format("delta").load(bronze_path)
+ .join(products_df, "product_id", "left")
+ .filter(col("event_date") == yesterday)
+)
+
+# First action β triggers full computation of base_df
+base_df.write.format("delta").mode("append").save(silver_path)
+
+# Second action β triggers FULL recomputation of base_df again
+base_df.groupBy("category").agg(sum("revenue")).write.format("delta").save(gold_path)
+```
+
+
+
+
+```python title="pipeline-with-cache.py"
+from pyspark import StorageLevel
+
+base_df = (
+ spark.read.format("delta").load(bronze_path)
+ .join(products_df, "product_id", "left")
+ .filter(col("event_date") == yesterday)
+)
+
+# Cache BEFORE the first action β base_df is used twice
+# MEMORY_AND_DISK: spills to disk if memory is full (safer than MEMORY_ONLY)
+base_df.persist(StorageLevel.MEMORY_AND_DISK)
+
+# First use β computes and stores base_df
+base_df.write.format("delta").mode("append").save(silver_path)
+
+# Second use β reads from cache, no recomputation
+base_df.groupBy("category").agg(sum("revenue")).write.format("delta").save(gold_path)
+
+# Always unpersist when done β frees executor memory for the next stage
+base_df.unpersist()
+```
+
+
+
+
+:::note
+We always use `MEMORY_AND_DISK` rather than `MEMORY_ONLY`. The reason: when memory fills up, `MEMORY_ONLY` silently drops the cached data and recomputes it on demand, you get none of the benefit and all of the overhead. We got burned by this once when a larger-than-usual dataset caused silent eviction mid-pipeline. `MEMORY_AND_DISK` spills the overflow to disk instead of evicting, which is slower than memory but far better than recomputing from scratch.
+:::
+
+**Result:** Eliminated one full recomputation of the join + filter stage. **~28 minutes saved.**
+
+## Mistake #3: Using the Wrong Join Strategy
+
+**Time lost to this mistake: ~62 minutes**
+
+Joins are the most expensive operation in distributed computing. When two datasets need to be joined, Spark has to get rows with matching keys onto the same machine which usually means moving large amounts of data across the network. That network movement is called a shuffle, and it's where most of the time in a join stage actually goes.
+
+PySpark supports three join strategies. Understanding which one to use and when is one of the highest-leverage optimizations available.
+
+### The Three Strategies
+
+**Sort-Merge Join (default for large tables)**
+Both datasets are shuffled so matching keys land on the same partition, then sorted, then merged. Correct for any size. Expensive because of the full shuffle.
+
+**Broadcast Join (best for large + small table)**
+The smaller table is collected to the driver and sent as a complete copy to every executor. The large table never moves. Dramatically faster when the small table fits comfortably in memory.
+
+**Bucket Join (best for repeated joins on the same key)**
+Both tables are pre-arranged on disk by join key at write time. When you join two bucketed tables on their bucket key, Spark skips the shuffle entirely, the data is already sitting where it needs to be. Expensive upfront, free on every subsequent join.
+
+
+
+
+
+
+```python title="join-before.py"
+# Spark defaults to Sort-Merge Join
+# products_df has 50,000 rows β tiny
+# But Spark doesn't know that and shuffles BOTH tables
+# 200M rows of sales_df shuffled across the network
+
+sales_df = spark.read.format("delta").load(bronze_path)
+products_df = spark.read.format("delta").load(products_path)
+
+enriched_df = sales_df.join(products_df, "product_id", "left")
+```
+
+
+
+
+```python title="join-after.py"
+from pyspark.sql.functions import broadcast
+
+sales_df = spark.read.format("delta").load(bronze_path)
+products_df = spark.read.format("delta").load(products_path)
+
+# Hint tells Spark to broadcast products_df to every executor
+# sales_df (200M rows) is NEVER shuffled
+# products_df (50K rows) is collected once and sent to all nodes
+enriched_df = sales_df.join(broadcast(products_df), "product_id", "left")
+```
+
+
+
+
+```python title="bucket-join.py"
+# Write tables once with bucketing β expensive upfront, free on every future join
+# Use when the same large-to-large join runs repeatedly
+
+sales_df.write \
+ .bucketBy(64, "product_id") \
+ .sortBy("product_id") \
+ .format("parquet") \
+ .saveAsTable("sales_bucketed")
+
+events_df.write \
+ .bucketBy(64, "product_id") \
+ .sortBy("product_id") \
+ .format("parquet") \
+ .saveAsTable("events_bucketed")
+
+# Now this join has ZERO shuffle β data is already co-located
+result = spark.table("sales_bucketed").join(
+ spark.table("events_bucketed"), "product_id"
+)
+```
+
+
+
+
+| Scenario | Strategy | Why |
+|---|---|---|
+| Large table + small table (< 200MB) | Broadcast join | Eliminates shuffle of large table |
+| Large table + large table, one-time | Sort-merge (default) | No alternative without pre-partitioning |
+| Large table + large table, repeated | Bucket join | Pre-pays shuffle cost once, eliminates it forever |
+| Skewed keys (a few keys have millions of rows) | Salting + broadcast | See tip below |
+
+
+
+
+:::tip
+**Join skew** is a related problem: when a small number of keys have a disproportionate number of rows, all that data lands on one executor which becomes a bottleneck while the rest of the cluster sits idle. The fix is **salting**: add a random integer (0βN) to the skewed key, replicate the smaller table N times with matching salt values, join on the salted key, then drop the salt column. This spreads the skewed key across N executors.
+:::
+
+**Result:** Switching the dimension join from sort-merge to broadcast eliminated the largest shuffle in the pipeline. **~62 minutes saved.**
+
+
+## Mistake #4: Writing Python UDFs Instead of Using Built-in Functions
+
+**Time lost to this mistake: ~38 minutes**
+
+Python UDFs (User Defined Functions) feel like a natural escape hatch. The built-in Spark functions don't cover what you need, so you write a Python function, decorate it with `@udf`, and move on. It works. It's just slow in a way that isn't immediately obvious and on a 200-million-row dataset, "not immediately obvious" can mean 38 extra minutes per run.
+
+### Why UDFs Are Expensive
+
+Here's what's actually happening when a Python UDF runs on a Spark cluster: PySpark lives on the JVM, and Python lives in a completely separate process. Every single row your UDF touches has to be packaged up, handed across a process boundary into the Python runtime, processed, and then packaged back up and handed back to the JVM. It's the equivalent of passing every item from a warehouse to a worker standing outside the building through a narrow window, one item at a time, both ways.
+
+We had three UDFs doing string cleaning on a 200-million-row DataFrame. Each UDF triggered that full cross-process handoff 200 million times. The functions themselves were trivial, a regex and some string lowercasing. The cost wasn't in the logic, it was in the 600 million window-handoffs happening around it.
+
+Built-in Spark functions (`pyspark.sql.functions`) don't have this problem. They run entirely inside the JVM alongside Spark's own engine, with no process boundary to cross and no per-row packaging overhead.
+
+
+
+
+
+```python title="udf-before.py"
+from pyspark.sql.functions import udf
+from pyspark.sql.types import StringType
+import re
+
+# Registered as a Python UDF
+# For 200M rows: cross-process handoff happens 200M times per UDF
+@udf(returnType=StringType())
+def clean_phone(phone):
+ if phone is None:
+ return None
+ digits = re.sub(r"\D", "", phone)
+ return digits if len(digits) == 10 else None
+
+@udf(returnType=StringType())
+def normalize_category(cat):
+ if cat is None:
+ return "unknown"
+ return cat.strip().lower().replace(" ", "_")
+
+df = (
+ df.withColumn("phone_clean", clean_phone(col("phone")))
+ .withColumn("category_norm", normalize_category(col("category")))
+)
+```
+
+
+
+
+```python title="udf-after.py"
+from pyspark.sql.functions import (
+ regexp_replace, when, length, trim, lower, col
+)
+
+# All native JVM execution β no cross-process overhead at all
+df = (
+ df
+ # Strip non-digits from phone
+ .withColumn("phone_digits", regexp_replace(col("phone"), r"\D", ""))
+ # Keep only 10-digit numbers, null otherwise
+ .withColumn(
+ "phone_clean",
+ when(length(col("phone_digits")) == 10, col("phone_digits")).otherwise(None)
+ )
+ # Normalize category: trim, lowercase, replace spaces
+ .withColumn(
+ "category_norm",
+ when(col("category").isNull(), "unknown")
+ .otherwise(
+ regexp_replace(lower(trim(col("category"))), " ", "_")
+ )
+ )
+ .drop("phone_digits")
+)
+```
+
+
+
+
+```python title="pandas-udf.py"
+# If no built-in equivalent exists, use a Pandas UDF (vectorized)
+# Pandas UDFs process data in Arrow batches, not row-by-row
+# Still crosses the process boundary, but once per batch instead of once per row
+
+from pyspark.sql.functions import pandas_udf
+from pyspark.sql.types import StringType
+import pandas as pd
+
+@pandas_udf(StringType())
+def complex_transform(series: pd.Series) -> pd.Series:
+ # This runs on batches of rows, not individual rows
+ # Use only when no built-in function covers your logic
+ return series.apply(lambda x: your_complex_logic(x) if x else None)
+
+df = df.withColumn("result", complex_transform(col("input_col")))
+```
+
+
+
+
+:::note
+The decision tree we follow for function choice:
+1. Does a `pyspark.sql.functions` built-in exist? β **Use it.**
+2. Does the logic involve complex Python libraries (ML models, regex with lookbehind, etc.)? β **Use a Pandas UDF.**
+3. Is there truly no alternative? β **Use a Python UDF, and leave a comment explaining why.**
+
+The vast majority of string cleaning, type casting, null handling, and conditional logic is covered by built-in functions. Check the [PySpark function docs](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/functions.html) before reaching for `@udf` β it takes 5 minutes and has saved us hours.
+:::
+
+**Result:** Replaced three Python UDFs with built-in equivalents. Stage runtime dropped from 41 minutes to 3 minutes. **~38 minutes saved.**
+
+## Mistake #5: Reading More Data Than Necessary
+
+**Time lost to this mistake: ~44 minutes**
+
+Before any transformation runs, the data has to come off storage and into Spark's memory. If you pull 180GB when you only need 2GB, you've already lost, no amount of smart transformation logic downstream recovers those wasted read operations.
+
+Two mechanisms cut data at the source: **predicate pushdown** and **column pruning**. Both work with Parquet and Delta Lake natively. Both get silently deactivated by small, easy-to-miss coding patterns.
+
+### Predicate Pushdown
+
+Imagine your Delta table as a library where each day's data lives in its own room, with the date on the door. Partition pruning is walking straight to yesterday's room. What we were doing instead was opening every room in the library, pulling every book off every shelf, carrying it all to a reading table, and then only reading the ones with yesterday's date on the spine before putting everything else back. The library was organized correctly. We just weren't reading the signs on the doors.
+
+With 90 days of history accumulated, we were reading 90x more data than the job actually needed on every single run. The fix is pushing the date filter into the read itself, so Spark can use the partition directory structure to skip everything irrelevant before a single file is opened.
+
+### Column Pruning
+
+Parquet stores data column by column, not row by row. This means if your table has 40 columns but your transformation uses 6, you can tell Spark to only load those 6 columns' physical data from disk. The other 34 are never touched. The catch: you have to select those columns at read time, not after a chain of transformations.
+
+
+
+
+```python title="read-before.py"
+from datetime import datetime, timedelta
+
+yesterday = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
+
+# Reads ALL columns from ALL partitions
+# Then filters in memory β after all 180GB is already read
+bronze_df = spark.read.format("delta").load(bronze_path)
+
+# Filter applied AFTER load β no partition pruning, no column pruning
+filtered_df = (
+ bronze_df
+ .filter(col("event_date") == yesterday)
+ .filter(col("status") == "completed")
+)
+
+# Selecting columns here is too late β data already read into memory
+result_df = filtered_df.select("event_id", "product_id", "revenue", "event_date")
+```
+
+
+
+
+```python title="read-after.py"
+from datetime import datetime, timedelta
+from pyspark.sql.functions import col
+
+yesterday = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
+
+# Column pruning: Spark reads ONLY these columns from Parquet files
+# Predicate pushdown: partition filter applied at file-reader level
+# Spark skips all partitions where event_date != yesterday
+bronze_df = (
+ spark.read.format("delta")
+ .load(bronze_path)
+ .select("event_id", "product_id", "revenue", "event_date", "status") # Prune columns first
+ .filter(col("event_date") == yesterday) # Partition pruning β activates at read time
+ .filter(col("status") == "completed") # Predicate pushdown into Parquet row groups
+)
+```
+
+
+
+
+```python title="verify-pushdown.py"
+# Confirm predicate pushdown is active β check the physical plan
+bronze_df.explain(mode="extended")
+
+# In the output, look for:
+# PartitionFilters: [isnotnull(event_date#12), (event_date#12 = 2026-05-15)]
+# PushedFilters: [IsNotNull(status), EqualTo(status,completed)]
+#
+# If you see PartitionFilters: [] β partition pruning is NOT active
+# If you see PushedFilters: [] β predicate pushdown is NOT active
+```
+
+
+
+
+:::note
+Two things both have to be true for partition pruning to work. First, the table must have been written with `partitionBy` on the column you're filtering. Second and this is the one that catches people, the filter must be on the partition column *as it exists in the table*, not on a renamed or derived version. We once spent an hour debugging a full scan that turned out to be caused by a `withColumnRenamed("event_date", "date")` sitting one line before the filter. The column name changed, Spark couldn't match it to the partition metadata, and pruning silently fell back to a full scan.
+:::
+
+**Result:** Data read dropped from ~180GB to ~2GB. Read + deserialization time fell from 47 minutes to 3 minutes. **~44 minutes saved.**
+
+## Mistake #6: Default Cluster Configuration
+
+**Time lost to this mistake: ~35 minutes (idle and wasted compute)**
+
+Even with perfect code, a misconfigured cluster leaves compute sitting idle. These settings control how many tasks run in parallel, how much memory each task gets, and whether the cluster actually uses all the hardware you're paying for.
+
+Beginners typically either accept the cloud provider's defaults without question, or paste settings from a Stack Overflow answer written for a different dataset and cluster size. Neither approach reflects the actual workload.
+
+### The Key Settings and What They Do
+
+**`spark.executor.memory`** - how much RAM each executor process gets. Too little and tasks start writing intermediate data to disk, which is dramatically slower. Too much and you've allocated headroom the executor can't use, while also giving the JVM garbage collector more memory to scan on every GC cycle.
+
+**`spark.executor.cores`** - how many tasks an executor runs simultaneously. We settled on 5 after testing: below 4, the executor's memory sits underutilized because there aren't enough concurrent tasks to fill it. Above 5, we started seeing storage I/O contention β too many tasks competing to read from the same disks at once. Five was the sweet spot for our setup, and it matches what we've seen hold up across different cluster sizes.
+
+**`spark.executor.instances`** - total number of executors. With autoscale on, this becomes a min/max bound rather than a fixed count.
+
+**`spark.driver.memory`** - the driver collects broadcast tables before distributing them to executors, so it needs more headroom than the default 1g allows. We had broadcast joins failing silently and falling back to sort-merge before we realized the driver was OOM-ing on the collection step.
+
+### Right-Sizing for Our 8GB Pipeline
+
+Our cluster: 4 worker nodes, each with 16 cores and 64GB RAM.
+
+```
+Available per node after OS overhead (~7GB): 57GB RAM, 15 cores
+Executor cores: 5 (our tested sweet spot)
+Executors per node: 15 Γ· 5 = 3 executors per node
+Memory per executor: 57GB Γ· 3 = 19GB (leave ~1GB headroom β set 18GB)
+Total executors: 3 Γ 4 nodes = 12 executors
+```
+
+
+
+
+```python title="cluster-default.py"
+# Default Spark config β unchanged from cluster creation
+# On our 4-node cluster, these settings leave most resources unused
+
+spark = SparkSession.builder \
+ .appName("SalesPipeline") \
+ .getOrCreate()
+
+# Defaults that hurt us:
+# spark.executor.memory = 1g (way too small β spills to disk constantly)
+# spark.executor.cores = 1 (only 1 task per executor β 15 cores idle per node)
+# spark.executor.instances = 2 (2 executors on a 4-node cluster β 50% idle)
+# spark.driver.memory = 1g (broadcast joins silently fall back to sort-merge)
+```
+
+
+
+
+```python title="cluster-tuned.py"
+spark = SparkSession.builder \
+ .appName("SalesPipeline") \
+ .config("spark.executor.memory", "18g") \
+ .config("spark.executor.cores", "5") \
+ .config("spark.executor.instances", "12") \
+ .config("spark.driver.memory", "8g") \
+ .config("spark.sql.shuffle.partitions", "64") \
+ .config("spark.sql.adaptive.enabled", "true") \
+ .config("spark.dynamicAllocation.enabled", "true") \
+ .config("spark.dynamicAllocation.minExecutors", "2") \
+ .config("spark.dynamicAllocation.maxExecutors", "12") \
+ .getOrCreate()
+```
+
+
+
+
+| Setting | Default | Our Value | Rule of Thumb |
+|---|---|---|---|
+| `spark.executor.memory` | 1g | 18g | (Node RAM β OS overhead) Γ· executors per node |
+| `spark.executor.cores` | 1 | 5 | 4β5 per executor (test on your setup) |
+| `spark.executor.instances` | 2 | 12 | (cores per node Γ· executor cores) Γ node count |
+| `spark.driver.memory` | 1g | 8g | 4β8g; higher if using large broadcasts |
+| `spark.sql.shuffle.partitions` | 200 | 64 | Total shuffle data size Γ· 128MB |
+
+
+
+
+:::tip
+For cloud clusters (Databricks, EMR, Dataproc), enable **dynamic allocation** instead of a fixed executor count. Dynamic allocation releases executors back to the pool during idle stages and acquires more when tasks are queuing β so a 3-minute light stage doesn't hold 12 executors that other jobs could use.
+
+```python
+spark.conf.set("spark.dynamicAllocation.enabled", "true")
+spark.conf.set("spark.dynamicAllocation.minExecutors", "2")
+spark.conf.set("spark.dynamicAllocation.maxExecutors", "12")
+```
+:::
+
+**Result:** Fully utilizing all 4 nodes reduced total wall-clock time by eliminating idle compute. Combined with eliminating disk spills from under-provisioned executors: **~35 minutes saved.**
+
+
+## Before and After Summary
+
+:::info
+
+| Mistake | Root Cause | Time Before | Time After | Saved |
+|---|---|---|---|---|
+| Wrong shuffle partition count | Default 200 partitions for 8GB dataset | 48 min | 11 min | **37 min** |
+| No caching on reused DataFrame | base_df computed twice from scratch | 28 min | <1 min | **28 min** |
+| Sort-merge join on dimension table | 50K-row table shuffled like a large table | 65 min | 3 min | **62 min** |
+| Python UDFs for string operations | Per-row cross-process overhead | 41 min | 3 min | **38 min** |
+| Full table scan on partitioned table | Filter applied after read, not at read time | 47 min | 3 min | **44 min** |
+| Default cluster config (1 core/executor) | 15 cores idle per node, constant disk spill | 45 min | 10 min | **35 min** |
+| **Total** | | **4h 12min** | **34 min** | **~3h 38min** |
+
+:::
+
+From 4 hours 12 minutes down to 34 minutes β an **86% reduction** on a pipeline doing exactly the same computation on exactly the same data.
+
+
+## PySpark Optimization Checklist
+
+Run through this before every pipeline goes to production.
+
+**Shuffle & Partitions**
+- [ ] Is `spark.sql.shuffle.partitions` set based on actual post-shuffle data size, not the default 200?
+- [ ] Is Adaptive Query Execution (`spark.sql.adaptive.enabled`) turned on?
+- [ ] Are there stages with a very large or very small number of tasks compared to the cluster size?
+
+**Caching**
+- [ ] Is any DataFrame referenced more than once? If yes β is it cached before the first action?
+- [ ] Is `.unpersist()` called after the cached DataFrame is no longer needed?
+- [ ] Is `StorageLevel.MEMORY_AND_DISK` used instead of `MEMORY_ONLY`?
+
+**Joins**
+- [ ] Is every join between a large and small table using `broadcast()`?
+- [ ] Is any large-to-large join repeated on the same key? If yes β is bucketing being used?
+- [ ] Are there any skewed keys? Check the Spark UI for tasks with 10xβ100x longer runtimes than others in the same stage.
+
+**Functions & UDFs**
+- [ ] Is every Python UDF replaceable with a `pyspark.sql.functions` built-in?
+- [ ] If a UDF is unavoidable, is it a Pandas UDF (vectorized) rather than a row-by-row Python UDF?
+
+**Reading Data**
+- [ ] Are only needed columns selected at read time (not `select *` after transformation)?
+- [ ] Is the partition filter applied immediately on the read result, on the partition column itself?
+- [ ] Does `df.explain()` show `PartitionFilters` and `PushedFilters` as non-empty?
+
+**Cluster Configuration**
+- [ ] Is `spark.executor.cores` set to 4β5 (not the default of 1)?
+- [ ] Is `spark.executor.memory` calculated from actual node RAM, not left at the 1g default?
+- [ ] Is dynamic allocation enabled for variable-length workloads?
+- [ ] Is `spark.driver.memory` set high enough to handle broadcast tables without OOM?
+
+---
+
+## Key Lessons
+
+**The Spark UI is your fastest debugging tool.** Every mistake above shows up in the Spark UI before you ever look at the code: long stage durations from wrong partition counts, skewed task distribution from join issues, tiny data sizes per task from over-partitioning, zero partition filters from missed pushdown. Open the UI first, read the physical plan second, look at the code third.
+
+**PySpark never stops you from writing the slow version.** The job runs either way. The only difference is whether it finishes in 34 minutes or 4 hours. Spark assumes you know what you're doing β which means the performance consequences of defaults are entirely invisible until you look for them.
+
+**Built-in functions exist for almost everything.** The instinct to reach for a Python UDF is understandable β Python is what most data engineers know best. But the `pyspark.sql.functions` module covers an enormous surface area: string manipulation, date arithmetic, array operations, conditional logic, window functions. A 5-minute search through the docs is almost always faster than the performance penalty of writing and maintaining a UDF.
+
+**Optimization compounds.** None of the six fixes above is independent. Fixing the partition count makes the join faster. Fixing the join makes caching more effective. Fixing the read makes everything upstream cheaper. Start with the fix that addresses the largest stage duration in the Spark UI and work down from there.
+
+
+## Frequently Asked Questions
+
+**Q: How do I know if my DataFrame is actually being cached or if Spark is silently dropping it?**
+A: The Storage tab in the Spark UI is the fastest way to check. Cached DataFrames show up there with their storage level, what fraction of the data was actually stored, and how much memory it consumed. If nothing shows up after an action runs, it either means the cache hasn't been triggered yet β caching is lazy, so it only materializes on the first action β or Spark evicted it because executor memory filled up. We switched everything to `MEMORY_AND_DISK` after getting burned by a silent eviction that caused a job to recompute a 20-minute stage we thought was cached. Under that storage level, Spark spills to disk instead of evicting, so you at least keep the result.
+
+**Q: Is the broadcast join threshold configurable? What if my "small" table is 300MB?**
+A: Yes, Spark's default auto-broadcast threshold is 10MB, which is conservative. We've raised it to 300MB on tables we know are stable in size:
+```python
+spark.conf.set("spark.sql.autoBroadcastJoinThreshold", str(300 * 1024 * 1024))
+```
+One thing we learned: don't go above 500MB without testing carefully. The driver has to collect the entire table into memory before broadcasting it out, and if you push that too high you'll see the driver OOM before the broadcast even starts and the error message isn't always obvious about what caused it.
+
+**Q: Should I always use `spark.sql.adaptive.enabled`? Are there downsides?**
+A: We run it on everything now and haven't regretted it. AQE has genuinely saved us from bad shuffle partition counts more than once, particularly on pipelines where the data volume varies day to day and our static estimate was off. The one scenario where we saw it cause slowdowns was on a particularly complex query plan with 20+ joins, where AQE's planning overhead added more time than the optimization saved. We turned it off for that specific job and kept it on everywhere else.
+
+**Q: How do I find join skew in the Spark UI?**
+A: Go to the Stages tab and look for a stage where the Max task duration is dramatically higher than the Median, anything above a 5x ratio is worth investigating. We had a stage once where the median task took 3 seconds and one task took 47 minutes. That's the classic skew signature: one executor holding a massive key while the rest of the cluster finishes and sits idle. Click into the stage, look at the task duration histogram, and if you see one bar far to the right while everything else clusters near zero, you've found it.
+
+**Q: What's the difference between `.cache()` and `.persist()`?**
+A: In practice, we always use `.persist(StorageLevel.MEMORY_AND_DISK)` explicitly and skip `.cache()` entirely. The behavior of `.cache()` has changed across Spark versions, in some versions it defaults to `MEMORY_ONLY`, in others `MEMORY_AND_DISK`. Rather than remember which version does what, we just use the explicit form. It takes four more characters to type and removes all ambiguity.
+
+**Q: Can I over-partition? Is more shuffle partitions always safer?**
+A: Yes, over-partitioning is a real problem and we've hit it. We had a pipeline where someone had set shuffle partitions to 1000 "to be safe" on a 4GB dataset. The Spark UI showed 1000 tasks completing in under 100ms each, the entire stage was task scheduling overhead, not computation. Spark's scheduler has to launch, track, and retire each task individually, and at 1000 tasks on 4GB of data, that bookkeeping cost more than the actual work. If you see a stage in the UI where every task completes in milliseconds, that's the sign you're over-partitioned. Drop the count by 4x and re-run.
+
+
+## References and Further Reading
+
+- [Apache Spark - Performance Tuning Guide](https://spark.apache.org/docs/latest/sql-performance-tuning.html)
+- [Apache Spark - Adaptive Query Execution](https://spark.apache.org/docs/latest/sql-performance-tuning.html#adaptive-query-execution)
+- [Delta Lake - Optimizations and Best Practices](https://docs.delta.io/latest/optimizations-oss.html)
+- [Databricks - Optimize PySpark Joins](https://learn.microsoft.com/en-us/azure/databricks/transform/optimize-joins)
+- [PySpark SQL Functions Reference](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/functions.html)
+- [RecodeHive - Azure Data Pipeline Cost Optimization](https://www.recodehive.com/blog/azure-cost-optimization)
+- [RecodeHive - Medallion Architecture Explained](https://www.recodehive.com/blog/medallion-architecture)
+- [RecodeHive - Hidden Cost of Streaming Pipelines](https://www.recodehive.com/blog/azure-cost-optimization)
+
+
+## About the Author
+
+**Aditya Singh Rathore** is a Data Engineer focused on building modern, scalable data platforms on Azure and Databricks. He writes about data engineering, cloud architecture, and real-world pipelines on [RecodeHive](https://www.recodehive.com/) turning hard-won production lessons into content anyone can apply.
+
+π [LinkedIn](https://www.linkedin.com/in/aditya-singh-rathore0017/) | [GitHub](https://github.com/Adez017)
+
+
+
- 
+
```
-
+
diff --git a/docs/Docker/intro.md b/docs/Docker/intro.md
index 87617e6c..e44ab7f2 100644
--- a/docs/Docker/intro.md
+++ b/docs/Docker/intro.md
@@ -13,7 +13,7 @@ tags:
description: Learn Docker fundamentals - what containers are, why use Docker, core concepts like images and containers, and get started with essential Docker commands.
---
-
- 
+
+
+
## Build Your First Pipeline: Step by Step
@@ -267,7 +267,7 @@ Your pipeline now runs automatically every night at 2am, copying new sales data
Let's step back and look at what you built:
-
+
This is the **Extract and Load** part of ETL. The file is extracted from the source container and loaded into the bronze layer, untouched, exactly as it arrived.
diff --git a/blog/azure-cost-optimization/img/01-azure-cost-before-after.png b/blog/azure-cost-optimization/img/01-azure-cost-before-after.png
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diff --git a/blog/azure-cost-optimization/index.md b/blog/azure-cost-optimization/index.md
new file mode 100644
index 00000000..7b71bd49
--- /dev/null
+++ b/blog/azure-cost-optimization/index.md
@@ -0,0 +1,465 @@
+---
+title: "Azure Data Pipeline Cost Optimization: How We Cut a $4,200 Bill by 73%"
+authors: [Aditya-Singh-Rathore]
+sidebar_label: "Azure Data Pipeline Cost Optimization"
+tags: [azure, cost-optimization, data-pipeline, adls-gen2, azure-synapse, apache-spark, azure-data-factory, delta-lake, data-engineering, cloud-cost]
+date: 2026-05-15
+
+description: Six costly mistakes in a 2GB Azure data pipeline drove a $4,200 monthly bill. Here's every mistake, root cause, and fix β with before-and-after numbers for each.
+
+draft: false
+canonical_url: https://www.recodehive.com/blog/azure-cost-optimization-data-pipelines
+
+meta:
+ - name: "robots"
+ content: "index, follow"
+ - property: "og:title"
+ content: "Azure Data Pipeline Cost Optimization: How We Cut a $4,200 Bill by 73%"
+ - property: "og:description"
+ content: "Six costly mistakes in a 2GB Azure data pipeline drove a $4,200 monthly bill. Here's every mistake, root cause, and fix β with before-and-after numbers."
+ - property: "og:type"
+ content: "article"
+ - property: "og:url"
+ content: "https://www.recodehive.com/blog/azure-cost-optimization-data-pipelines"
+ - property: "og:image"
+ content: "./images/cover.png"
+ - name: "twitter:card"
+ content: "summary_large_image"
+ - name: "twitter:title"
+ content: "Azure Data Pipeline Cost Optimization: How We Cut a $4,200 Bill by 73%"
+ - name: "twitter:description"
+ content: "Six costly mistakes, one pipeline, 2GB of data, a $4,200 bill. Here's how we fixed all of them."
+ - name: "twitter:image"
+ content: "./images/cover.png"
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+
+
+# Azure Data Pipeline Cost Optimization: How We Cut a $4,200 Bill by 73%
+
+The Azure billing email arrived on the first of the month. **$4,247.83.**
+
+Our pipeline processed roughly 2GB of sales data per day and served a Power BI dashboard to 30 users. There was no logical reason for a bill that size. Over the next three days, a line-by-line audit of Azure Cost Analysis revealed not one big mistake but **six medium-sized ones**, each silently running up costs in parallel, invisible until the invoice arrived.
+
+This post is that investigation: every mistake explained, every fix documented, and the exact before-and-after numbers. If you're building data pipelines on Azure and haven't audited your costs recently, at least one of these is probably happening to you right now.
+
+**What you'll learn in this post:**
+- Why a Dedicated SQL Pool running 24/7 is the single most expensive default mistake in Azure Synapse
+- How to replace nightly full loads with watermark-based incremental loads in Azure Data Factory
+- How to right-size Spark pools and configure auto-termination to stop paying for idle compute
+- How partition pruning on Delta Lake tables can reduce data scanned by over 90%
+- How ADLS Gen2 lifecycle policies passively save money on storage with zero ongoing effort
+- When a scheduled micro-batch replaces a 24/7 streaming pipeline without any business impact
+
+
+
+## The Pipeline Architecture
+
+Before the mistakes make sense, here is the full pipeline:
+
+```
+Daily sales data from REST API (~2GB/day)
+ β
+ADF Pipeline (ingestion)
+ β
+ADLS Gen2 β bronze/ (raw Parquet files)
+ β
+Spark job (transformation)
+ β
+ADLS Gen2 β silver/ (Delta tables)
+ β
+Dedicated SQL Pool (serving layer)
+ β
+Power BI dashboard (30 users)
+```
+
+A standard [Medallion Architecture](https://www.recodehive.com/blog/medallion-architecture), nothing exotic. 2GB of data per day. 30 users. Should have cost a few hundred dollars a month at most. It cost **~$4,247.83**. Here is exactly why.
+
+
+## Mistake #1: Dedicated SQL Pool Running 24/7
+
+**Monthly cost of this mistake: ~$1,800**
+
+This was the single largest line item. A Dedicated SQL Pool at DW200c was provisioned to serve the Power BI dashboard and left running continuously 24 hours a day, 7 days a week because auto-pause had never been configured.
+
+The thing that surprised me most when I first dug into the bill was this: the SQL Pool charged us the same rate at 3am on a Saturday as it did during peak usage on a Tuesday afternoon. I had assumed, naively, that there was some kind of idle detection built in. There isn't. When it's provisioned, you're paying - full stop, whether a single query runs or not. Our 30 users were active between 9am and 6pm on weekdays, 45 hours of actual usage per week. The pool was running for 168 hours per week. That's 123 hours of idle, fully-billed compute every single week, and it showed up on our invoice as a flat $1,800 charge with no breakdown by usage.
+
+:::note
+The SQL Pool doesn't throttle billing when it's idle, provisioned capacity is billed by the hour regardless of query activity. Pausing the pool is the only way to stop the DWU clock. Storage costs continue when paused, but the compute component which is the large part, stops completely.
+:::
+
+### The Fix: Auto-Pause with Azure Automation Runbooks
+
+The solution is two Azure Automation runbooks, one to pause the pool at the end of business hours, one to resume it in the morning. The runbooks use Managed Identity for authentication, which avoids hardcoding credentials.
+
+```python title="pause-sql-pool.py"
+# Azure Automation Runbook β pause Synapse SQL Pool outside business hours
+from azure.identity import ManagedIdentityCredential
+from azure.mgmt.synapse import SynapseManagementClient
+
+credential = ManagedIdentityCredential()
+client = SynapseManagementClient(credential, subscription_id)
+
+# Pause at 7pm weekdays
+client.sql_pools.begin_pause(
+ resource_group_name,
+ workspace_name,
+ sql_pool_name
+)
+```
+
+Schedule the pause runbook at 7pm weekdays and the resume runbook at 8am. Weekends stay paused unless a manual override is triggered through the Azure portal.
+
+**Result:** Billed hours dropped from 720 to roughly 210 per month. SQL Pool cost fell from ~$1,800/month to ~$530/month, a saving of **$1,270/month**.
+
+:::tip
+If your workload is exploratory rather than dashboard-serving, consider whether [Serverless SQL Pool](https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/on-demand-workspace-overview) is sufficient. Serverless pools bill per TB of data scanned rather than provisioned DWUs, which can be significantly cheaper for infrequent query patterns.
+:::
+
+
+## Mistake #2: Full Load Running Every Night Instead of Incremental
+
+**Monthly cost of this mistake: ~$620**
+
+The ADF pipeline was configured to pull **all records** from the source database on every nightly run not just new or updated ones. Day 1 it pulled 2GB. By Day 60 it was pulling 120GB, processing records that had already been processed 59 times before.
+
+This pattern is extremely common and extremely expensive. Every night, the ADF pipeline read the entire historical dataset, the Spark transformation job processed all of it, and the results were written back to Delta. The billable compute scaled with the dataset size, not with the actual volume of new data.
+
+### The Fix: Watermark-Based Incremental Loading
+
+A watermark stores the timestamp of the last successfully processed record. Every pipeline run reads only records newer than that timestamp, then updates the watermark on success.
+
+The implementation in ADF uses two Lookup activities and a query parameterized by the watermark value:
+
+ +
+
+
[](https://github.com/sanjay-kv/Open-source-Practice)
-
Take a moment to:
@@ -68,12 +68,12 @@ Forking creates your own copy of the repository where you can make changes witho
[](https://github.com/sanjay-kv/Learn-GitHub/fork)
-
[](https://github.com/sandemouser/Learn-GitHub)
-
Once the fork is complete, you'll see:
@@ -82,7 +82,7 @@ Once the fork is complete, you'll see:
[](https://github.com/sandemouser/Learn-GitHub)
-
## Step 3: Edit files directly on GitHub
@@ -101,7 +101,7 @@ For this practice repository, you'll typically add your name to a contributors l
[](https://github.com/sandemouser/Learn-GitHub)
-
Common first contributions include:
@@ -113,7 +113,7 @@ Common first contributions include:
[](https://github.com/sandemouser/Learn-GitHub)
-
:::tip Best Practices for Making Changes
@@ -140,7 +140,7 @@ After making your edits, you need to commit them. A commit is like saving a snap
[](https://github.com/sandemouser/Learn-GitHub)
-
**Good commit message examples:**
@@ -155,7 +155,7 @@ After making your edits, you need to commit them. A commit is like saving a snap
[](https://github.com/sandemouser/Learn-GitHub)
-
## Step 5: Create a Pull Request
@@ -188,7 +188,7 @@ A Pull Request is a way to propose changes to a repository. It allows:
[](https://github.com/sandemouser/Learn-GitHub)
-
### Writing a Good Pull Request Description
@@ -281,7 +281,7 @@ After creating your pull request:
For a visual walkthrough of the entire process, watch this helpful video:
-
+
## Finding Projects to Contribute To
diff --git a/docs/GitHub/GitHub-basics/github-repo-command-line.md b/docs/GitHub/GitHub-basics/github-repo-command-line.md
index 8cdaba65..fd790306 100644
--- a/docs/GitHub/GitHub-basics/github-repo-command-line.md
+++ b/docs/GitHub/GitHub-basics/github-repo-command-line.md
@@ -110,7 +110,7 @@ git config --global user.name "Github username"
-
+
## Conclusion
diff --git a/docs/GitHub/Maintainer-guide/enable-discussion.md b/docs/GitHub/Maintainer-guide/enable-discussion.md
index ee037026..6408d82c 100644
--- a/docs/GitHub/Maintainer-guide/enable-discussion.md
+++ b/docs/GitHub/Maintainer-guide/enable-discussion.md
@@ -129,7 +129,7 @@ Now you will be able to see the uploaded resume.
### Watch the video Tutorial
-
+
## Conclusion
diff --git a/docs/GitHub/Maintainer-guide/github-labels.md b/docs/GitHub/Maintainer-guide/github-labels.md
index 8b1fbaaa..b861787c 100644
--- a/docs/GitHub/Maintainer-guide/github-labels.md
+++ b/docs/GitHub/Maintainer-guide/github-labels.md
@@ -123,7 +123,7 @@ Now you will be able to see the uploaded resume.
### Watch the video Tutorial
-
+
## Conclusion
diff --git a/docs/GitHub/Maintainer-guide/github-project.md b/docs/GitHub/Maintainer-guide/github-project.md
index 5841bc52..97ddcba9 100644
--- a/docs/GitHub/Maintainer-guide/github-project.md
+++ b/docs/GitHub/Maintainer-guide/github-project.md
@@ -131,7 +131,7 @@ Now you will be able to see the uploaded resume.
### Watch the video Tutorial
-
+
## Conclusion
diff --git a/docs/GitHub/Maintainer-guide/milestone.md b/docs/GitHub/Maintainer-guide/milestone.md
index 86d59756..e271f578 100644
--- a/docs/GitHub/Maintainer-guide/milestone.md
+++ b/docs/GitHub/Maintainer-guide/milestone.md
@@ -127,7 +127,7 @@ Now you will be able to see the uploaded resume.
### Watch the video Tutorial
-
+
## Conclusion
diff --git a/docs/GitHub/intro-github.md b/docs/GitHub/intro-github.md
index 895359d4..09a63f8d 100644
--- a/docs/GitHub/intro-github.md
+++ b/docs/GitHub/intro-github.md
@@ -14,93 +14,106 @@ tags:
description: In this tutorial, you will learn about GitHub, its importance, what is GitHub from Scratch, how to use GitHub, steps to start using GitHub, and more.
---
-GitHub is a web-based platform used for version control and collaboration. It allows developers to work together on projects from anywhere in the world. GitHub is built on top of Git, a distributed version control system created by Linus Torvalds in 2005.
+GitHub is a cloud-based platform that helps developers store, manage, and track changes to their code. It combines the power of Git β a version control system β with a collaborative interface that makes teamwork across distributed teams possible. Whether you are building a solo project or contributing to a large open-source codebase, GitHub provides the tools to manage your work effectively.
:::note
Key Features of GitHub:
-GitHub will help you to control the version of the project on a large scale.
+- GitHub enables large-scale version control, letting teams track every change made to a project over time.
+- It supports both public and private repositories, making it suitable for open-source projects and proprietary software alike.
+- GitHub offers paid plans with additional features like advanced security, larger storage, and team management tools.
+:::
-GitHub offers Premium account for use, Premium users get unlimited GitHub Repo time, Storage etc.
+---
-:::
+## How to Start with GitHub?
-## How to start with GitHub?
+As a developer, you often want to share your work, get feedback, and collaborate with others. GitHub makes this possible by providing a central place where code can be hosted, reviewed, and improved together. Instead of emailing files back and forth, teams can work on the same codebase simultaneously without overwriting each other's work.
-Whenever you create something exciting or something new on your own, you always want to show it to others. As programmers, we also want to show our projects and codes to others, but how? So GitHub is the solution, it let's people to collaborate and scale the projects all on cloud. Some other example of similar platform are Bitbucket, GitLab, Beanstalk, etc. Many of them give you the facility to work on your code together as a team. Currently, GitHub is more popular than other above-listed platforms for hosting your code.
-:::info
-In the picture below, you can see Developer 1, Developer 2, etc., working on the same project. Let's say they are trying to build an Amazon website; Developer 1 handles the men's shopping section, Developer 2 deals with the women's section, and Developer 3 works on the login feature.
+Other platforms that offer similar functionality include Bitbucket, GitLab, and Azure Repos. Each has its strengths, but GitHub remains the most widely adopted platform for hosting open-source projects and developer portfolios.
-So, each individual works on a different feature from a copy of the central repository, once the development is done they push changes to the *remote repository* (central repo as per picture). Once a code review happens and it is good to go, the senior developer will merge the code into the central repository so all the features will be live in production.
+:::info
+Consider a team building an e-commerce website. Developer 1 works on the product listing page, Developer 2 handles the checkout flow, and Developer 3 builds the user authentication system. Each developer works on their own copy of the codebase locally. When their feature is ready, they push the changes to the shared remote repository on GitHub. A senior developer then reviews the code through a pull request and merges it into the main branch, making the new features available to everyone.
:::
-
- [](https://code.visualstudio.com/)
-
-
+
+ [](https://github.com/)
+
+---
-## What is Git??
+## What is Git?
-In the above example, all the developers were able to work on different features simultaneously because of Git. For uploading your project to your GitHub account, you need to install Git first. In other words, Git helps with the version management of files and coordinates work among a diverse team in the software development phase. Git is an open-source project and developed in 2005 by Linus Torvalds and Junior.
+Git is the engine that powers GitHub. It is a free, open-source distributed version control system created by Linus Torvalds in 2005. Torvalds originally built Git to manage the development of the Linux kernel, which required a system that could handle thousands of contributors efficiently.
+Git tracks changes to files over time, allowing developers to revisit earlier versions of their code, understand what changed and when, and coordinate work across a large team without conflicts.
-
:::info
-1. *What is Version control System?*: To understand the version control system, let me give you one example; sometimes, you wish you have a record that contains all the changes you made in your code or your project. The version control systems are software tools that record all of your changes in the files. It is like a database of changes. Git is a version control system that most developers prefer to use. We will see how you can upload your code on GitHub using Git. With VCS you can track the branch, who made changes at what time, line of code added, retrieve the previous version of changes.
-2. Basic Git Terminology::
-
- | Keyword | Terminology | Description |
- | --- | --------------- | -------------------- |
- | VCS | Version Control System | Track changes to a collection of files |
- | SCM | Software configuration Management | Another name for VCS,earlier versions of VCS, like CVS and SVN, used a centralized server, which caused a single point of failure. |
- | DVCS | Distributed Version Control System| Git is distributed, the project history is stored both in client and server. Means you can make changes locally and remote. |
- | Working Tree | Tree | Current version of files where the active project is on |
- | Repo | Repository | Top of the working tree where Git keeps all records and history. |
- | Bare repo | | Not part of working Tree ends with .git, eg. backup.git |
- | Hash | 160 bit long SHA-1 | Based on the hash number, the file has been modified or not. |
- | Object | Git has 4 objects | Tree Object(directory, names), Blob Object(main file), Commit Object(specific versions), Tag(name attached to commit) |
- | Commit | | Makes Changes |
- | Branch | A series of linked commits | The most recent commit is called Head. |
- | Remote | | reference to other git repo |
- | | Git Command | Check our [Git Commands Cheatsheet](../GitHub/setup-environment/git-commands.md) for practical examples |
-
+**What is a Version Control System?**
+
+Imagine you are working on a project and you accidentally delete an important function. Without version control, recovering that code would be difficult or impossible. A version control system (VCS) acts like a detailed history log β every change you make is recorded, along with who made it and when. You can roll back to any earlier state at any point.
+
+Git is a distributed version control system (DVCS), which means every developer has a full copy of the project history on their local machine. This makes it faster and more resilient than older centralized systems.
+
+**Core Git Terminology:**
+
+| Keyword | Terminology | Description |
+| --- | --------------- | -------------------- |
+| VCS | Version Control System | Tracks changes to a collection of files over time |
+| SCM | Software Configuration Management | Another term for VCS; earlier systems like CVS and SVN used centralized servers |
+| DVCS | Distributed Version Control System | Each developer holds a full copy of the project history locally and remotely |
+| Working Tree | Tree | The current state of files in your active project directory |
+| Repo | Repository | The top-level folder where Git stores all project history and records |
+| Bare Repo | | A repository without a working tree, typically used for remote backups; ends with `.git` |
+| Hash | 160-bit SHA-1 | A unique identifier generated for each commit to detect changes |
+| Object | Git has 4 object types | Tree (directories), Blob (file contents), Commit (snapshots), Tag (named references) |
+| Commit | | A saved snapshot of changes at a point in time |
+| Branch | A series of linked commits | An independent line of development; the latest commit is called HEAD |
+| Remote | | A reference to a Git repository hosted on another machine or server |
+| | Git Commands | See our [Git Commands Cheatsheet](../GitHub/setup-environment/git-commands.md) for practical examples |
:::
:::tip Need Git Commands?
-Want to start using Git right away? Check out our [comprehensive Git Commands Cheatsheet](../GitHub/setup-environment/git-commands.md) that includes 50 essential Git commands with examples. We also recommend trying [Learn Git Branching](https://learngitbranching.js.org/) - an interactive visual tool to practice Git commands in a gamified environment!
+Ready to start using Git? Check out our [comprehensive Git Commands Cheatsheet](../GitHub/setup-environment/git-commands.md) with 50 essential commands and real examples. We also recommend [Learn Git Branching](https://learngitbranching.js.org/) β an interactive visual tool to practice Git in a gamified environment.
:::
-## Why Learn GitHub?
+---
-GitHub simplifies the command-line interface of Git and makes it more GUI-friendly. GitHub is built on top of Git. Here want we do is staging the files and doing the commit, You can clone the entire repository, create a branch, commit to that branch, and then ask the main developer to merge the branch. When you clone in reality you are creating a copy of the real code/repository in your local environment.
+## Why Learn GitHub?
- 
-
-
-:::info
-1. Basic GitHub Keywords::
+GitHub builds on top of Git by adding a visual interface, collaboration features, and a large developer community. Here is why learning GitHub is valuable:
- | Category | Description |
- | --- | -------------------- |
- | Issues | The place where new suggestions or development ideas can be added. |
- | Discussions | Place where a community discussion can happen |
- | Pull requests | PR - Once the user worked on the issue, they can develop the feature and ask to merge |
- | Labels | Feature to categorise the issue, like bug, deadline, feature |
- | Actions | To automate the entire process of repeitive task on GitHub |
- | Forks | Cloning the original repo to your name |
+1. **Industry standard** β The majority of open-source projects and many private companies use GitHub to manage their codebases.
+2. **Portfolio visibility** β Your GitHub profile acts as a public portfolio that employers and collaborators can view.
+3. **Collaboration tools** β Features like pull requests, code reviews, and issue tracking make teamwork structured and transparent.
+4. **Automation** β GitHub Actions allows you to automate repetitive tasks like testing, building, and deploying your application.
+5. **Community** β GitHub hosts millions of open-source projects, making it a great place to learn from real codebases and contribute to projects you use.
+The typical GitHub workflow looks like this: you clone a repository to your local machine, create a new branch for your feature, make and commit your changes, push the branch to GitHub, and then open a pull request asking the project maintainer to review and merge your work.
-The fork is a personal copy of the repo which is already present or uploaded in GitHub by a different user. Once you fork and make changes to the repository the changes will happen to your forked repo, not the real one.
+
+:::info
+**Core GitHub Concepts:**
+
+| Feature | Description |
+| --- | -------------------- |
+| Issues | A place to report bugs, request features, or track tasks |
+| Discussions | A space for community conversations and questions |
+| Pull Requests | A request to merge your changes into the main codebase after review |
+| Labels | Tags used to categorize issues and pull requests (e.g. bug, enhancement) |
+| Actions | Automation workflows that run on events like push or pull request |
+| Forks | A personal copy of someone else's repository under your own account |
+
+A fork is useful when you want to contribute to a project you do not have direct write access to. You fork the repository, make your changes in your own copy, and then submit a pull request to the original project.
:::
+---
+### Watch the Video Tutorial
+
-### Watch the video Tutorial
-
-
-
+---
## Conclusion
-If you think this GitHub Tutorial starter kit for Beginner was useful to you, then donβt forget to share it with others. We will discuss this in detail in the next post. or In the next post, we will discuss how to upload your project to your GitHub account using Git.
+GitHub is far more than a place to store code β it is a complete platform for software collaboration, project management, and automation. By learning GitHub, you gain a skill that is relevant across every area of software development, from personal projects to large enterprise teams. In the next tutorial, we will walk through how to create your first GitHub repository and push your local project to it using Git.
\ No newline at end of file
diff --git a/docs/GitHub/intro-gitlab.md b/docs/GitHub/intro-gitlab.md
deleted file mode 100644
index 287d9269..00000000
--- a/docs/GitHub/intro-gitlab.md
+++ /dev/null
@@ -1,94 +0,0 @@
----
-id: intro-gitlab
-title: Introduction of GitLab
-sidebar_label: Introduction of GitLab #displays in sidebar
-sidebar_position: 2
-tags:
- [
- GitLab,
- Introduction of GitLab,
- Why learn GitLab,
- How to use GitLab,
- GitLab Prerequisites,
- ]
-description: In this tutorial, you will learn about GitLab, its importance, why learn GitLab, how to use GitLab, prerequisites to get started, and more.
----
-
-GitLab is a web-based DevOps lifecycle tool that provides a **Git repository manager**, issue tracking, continuous integration/continuous deployment (CI/CD) pipelines, and more. It is similar to GitHub but focuses heavily on providing an **all-in-one platform** for software development and DevOps.
-
-:::note
-Key Features of GitLab:
-- GitLab is not only a Git-based repository management tool but also provides **built-in CI/CD pipelines**.
-- Unlike GitHub, GitLab can be **self-hosted** as well as used on the cloud (GitLab.com).
-- GitLab offers Premium Plans with advanced CI/CD, security, compliance, and scalability features.
-:::
-
----
-
-## How to start with GitLab?
-
-When developers create new projects or collaborate on existing ones, they need a place to host, version, and automate their workflows. GitLab provides this with the **additional advantage of integrated DevOps tools** such as issue boards, pipelines, and monitoring.
-
-Other alternatives include GitHub, Bitbucket, and Azure Repos. GitLab is often preferred in organizations where **self-hosting** and **end-to-end DevOps** automation are critical.
-
-:::info
-In the picture below, you can see multiple developers working on the same project. For example, Developer 1 handles the backend APIs, Developer 2 builds the frontend interface, and Developer 3 manages CI/CD pipeline setup.
-
-Each developer works on their local copy of the repository and pushes their changes to the **remote GitLab repository**. After a merge request (MR) review, the maintainer merges the changes into the main branch. GitLab can then automatically trigger a **pipeline** to build, test, and deploy the new version of the project.
-:::
-
-
- [](https://about.gitlab.com/)
-
-
----
-
-## Why Learn GitLab?
-
-GitLab is more than just version controlβit provides a complete DevOps platform.
-Hereβs why you should learn it:
-
-1. **Integrated DevOps** β GitLab includes planning, coding, testing, security, deployment, and monitoring in one place.
-2. **Self-hosting option** β Unlike GitHub, GitLab allows organizations to run GitLab on their own servers.
-3. **Automation with CI/CD** β GitLabβs pipelines make testing and deployment faster and less error-prone.
-4. **Collaboration** β Teams can manage issues, boards, milestones, and code reviews easily.
-5. **Industry demand** β Many companies prefer GitLab for its **security compliance** and DevOps workflows.
-
----
-
-## How to Learn GitLab?
-
-To get started with GitLab, here are the recommended steps and prerequisites:
-
-:::info
-### π Prerequisites:
-- **Basic Git Knowledge**: Understand commands like `git clone`, `git add`, `git commit`, `git push`.
-- **Programming Knowledge**: Any language is fine (Python, JavaScript, Java, etc.).
-- **Command Line Basics**: Comfortable using a terminal (Linux, macOS, or Windows PowerShell).
-- **Optional (for CI/CD)**: Docker basics, YAML syntax (for writing pipeline configs).
-
----
-
-### π Learning Path:
-1. **Create an account** on [GitLab.com](https://gitlab.com/).
-2. **Install Git** on your system to interact with GitLab repositories.
-3. **Start a new project** or fork an existing repository.
-4. **Explore GitLab features**: issues, merge requests, labels, and boards.
-5. **Learn GitLab CI/CD** by creating `.gitlab-ci.yml` for automating builds/tests.
-6. **Advance**: Learn GitLab Runners, environment variables, and deployment.
-
-
-:::
-
----
-
-### Watch the video Tutorial
-
-
----
-
-## Conclusion
-
-GitLab is not just a code hosting toolβitβs a **complete DevOps ecosystem**. By learning GitLab, you gain both Git skills and practical experience with **automation pipelines, deployments, and project management tools**.
-
-In the next tutorial, we will explore how to create your first GitLab project, push code to it, and set up a CI/CD pipeline.
diff --git a/docs/GitHub/setup-environment/git-commands.md b/docs/GitHub/setup-environment/git-commands.md
index bbf822f8..02cc4dd5 100644
--- a/docs/GitHub/setup-environment/git-commands.md
+++ b/docs/GitHub/setup-environment/git-commands.md
@@ -182,7 +182,7 @@ git push -u origin main #Push changes to remote repository and remember the bran
:::
-
+
## Practice Git Interactively
diff --git a/docs/GitHub/setup-environment/setup-environment.md b/docs/GitHub/setup-environment/setup-environment.md
index 6f0fcdcd..233f5dfe 100644
--- a/docs/GitHub/setup-environment/setup-environment.md
+++ b/docs/GitHub/setup-environment/setup-environment.md
@@ -14,9 +14,9 @@ tags:
description: In this tutorial, you will learn how to set up your development environment for Git And GitHub.
---
-GitHub is a platform for version control of your software development using Git. GitHub was founded in 2008 and was recently acquired by Microsoft, who is now the parent organization. Setting up is a straightforward process: first, create a GitHub account online, then install Git on your system and connect it with GitHub. This way, any changes made will be pushed directly to GitHub.
+GitHub is a code hosting platform built on top of Git. It was founded in 2008 and acquired by Microsoft in 2018. If you are just getting started, the setup is simple, you create a free account on GitHub's website, install Git on your machine, and link the two together so your local changes can be pushed to the cloud.
-### Step 1: Letβs create a GitHub Account
+### Step 1: Let's create a GitHub Account
To create a GitHub account, go to the [GitHub Website](https://github.com/) and sign up using your email ID.
@@ -26,14 +26,14 @@ To create a GitHub account, go to the [GitHub Website](https://github.com/) and
### Step 2: Finishing the account creation
-Choose a readable username; this is important, just like your LinkedIn username. After completing the signup process, you may be prompted to solve a puzzle as part of the verification.
+Pick a username that is easy to read and remember, think of it the same way you would choose a professional username on LinkedIn. Once you complete the signup form, GitHub will ask you to solve a quick puzzle to confirm you are human.
**Live Server:** GitHub signup screen
[](https://github.com/)
-**Features you receive:** On completing registration, you gain access to GitHub Copilot, unlimited repositories, built-in tools for code quality improvement, automated workflows with Actions, and support from the community.
+**What you get with a free account:** Once registered, you can create unlimited public and private repositories, use GitHub Copilot for coding assistance, set up automated workflows with GitHub Actions, and access a large developer community for support and collaboration.
[](https://github.com/)
@@ -44,7 +44,7 @@ Click the top right profile icon, then select "Your profile" to view your profil
### Step 3: Understanding the Interface
-As the next step, GitHub will ask you to verify your account. Check your email inbox for a verification mail, and confirm to proceed. After verification, you will be redirected to your GitHub page (example below), which will show your verified status.
+GitHub will send a verification email to the address you signed up with. Open that email and click the confirmation link. Once verified, you will be taken to your GitHub dashboard where your account status will show as confirmed.
[](https://github.com/sanjay-kv)
@@ -52,30 +52,30 @@ As the next step, GitHub will ask you to verify your account. Check your email i
### Step 4: Personalize and Secure Your Account
-After verifying your email and accessing your GitHub dashboard, take a moment to personalize and secure your account. Add a profile picture to make your account recognizable, fill out your bio with your background or interests, and link relevant social media or websites. Review your account security settingsβenable two-factor authentication to strengthen your protection. Updating profile details and enabling security features ensures a professional, secure GitHub presence for future collaborations.
+With your account verified, spend a few minutes making it your own. Upload a profile photo so collaborators can recognize you, write a short bio describing what you work on, and add links to your website or other profiles. While you are in settings, head over to the security section and turn on two-factor authentication this is covered in detail in the next step.
-### Step 5: Final Step β Secure Your GitHub Account
+### Step 5: Final Step: Secure Your GitHub Account
-Congratulations on setting up your GitHub account! Before exploring further, itβs critical to secure your account using multi-factor authentication (2FA).
+Congratulations on setting up your GitHub account! Before you start exploring, take a few minutes to turn on two-factor authentication (2FA). This is one of the most effective ways to protect your account.
-**Why enable multi-factor authentication (2FA)?**
-2FA adds an extra layer of security by requiring both your password and a unique verification code from your mobile device whenever you sign in. This prevents unauthorized access, even if your password is compromised, keeping your code and personal information safe.
+**Why does 2FA matter?**
+When you log in with only a password, anyone who gets hold of that password can access your account. With 2FA turned on, logging in requires a second step a short code generated by an app on your phone. Even if someone steals your password, they still cannot get in without that code.
-**How to enable 2FA on GitHub:**
+**How to turn on 2FA:**
-1. Click your profile picture in the top right, then click **Settings**.
-2. In the left sidebar under **Access**, select **Password and authentication**.
-3. Find the **Two-factor authentication** section and click **Enable two-factor authentication**.
-4. Choose your preferred method (an authenticator app is recommended), and scan the displayed QR code with the app on your phone.
-5. Enter the code generated by your authenticator app, or use an SMS code if you chose that method.
-6. Save your recovery codes in a safe placeβthese allow you to regain access if you lose your phone.
-7. Confirm that you have saved your recovery codes and finish setup.
+1. Click your profile picture in the top right corner and open **Settings**.
+2. In the left menu, look under **Access** and click **Password and authentication**.
+3. Scroll to the **Two-factor authentication** section and click the button to enable it.
+4. Select an authenticator app as your method, this is more reliable than SMS. Open the app on your phone and scan the QR code shown on screen.
+5. Type in the six-digit code your app generates to confirm everything is connected.
+6. GitHub will give you a set of recovery codes. Save these somewhere safe, a password manager or a printed copy stored securely. These are the only way back in if you ever lose access to your phone.
+7. Complete the setup and you are done.
[](https://github.com/sanjay-kv)
-Now, 2FA is enabled on your account. Every time you log in, youβll need both your password and a unique codeβmaking your account far more secure.
+From this point on, every login will ask for both your password and a fresh code from your authenticator app.
**Dashboard recap:**
@@ -88,6 +88,6 @@ Now, 2FA is enabled on your account. Every time you log in, youβll need both y
## Conclusion
-Thank you for reading βHow to create a GitHub Account?β. In the next post, you'll learn how to use Git to create a repository and clone a project from GitHub.
+Thank you for reading "How to create a GitHub Account?". In the next post, you'll learn how to use Git to create a repository and clone a project from GitHub.
Signing off, Sanjay Viswanathan.
diff --git a/docs/Technical/intro-github.md b/docs/Technical/intro-github.md
index dc66c28e..a08400c8 100644
--- a/docs/Technical/intro-github.md
+++ b/docs/Technical/intro-github.md
@@ -94,7 +94,7 @@ The fork is a personal copy of the repo which is already present or uploaded in
### Watch the video Tutorial
-
+
## Conclusion
diff --git a/docs/getting_started.md b/docs/getting_started.md
index 7265606a..ed269f7c 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -61,6 +61,28 @@ Use a short, descriptive name β for example `blog/intro-to-docker` or `blog/re
---
+:::tip Blog Quality Checklist
+Before starting any development, make sure your blog meets **all** of the following criteria. Your blog can be **rejected** if any requirement is not fulfilled:
+
+- 1. 5 backlinks to different external websites to support our documentation.
+- 2. 5 internal backlinks to other articles on recodehive.
+
+- 3. **No generic content** β avoid surface-level topics like "what is Azure" or "difference between X and Y". Write pure, high-depth technical articles with images. See [this example](https://www.recodehive.com/docs/GitHub/Maintainer-guide/milestone) for the standard we aim for. (Tip: tools like [Snagit](https://www.techsmith.com/screen-capture.html) help produce great annotated screenshots.)
+
+- 4. Image filenames must be descriptive and SEO-friendly β no random names like `screenshot123.png`.
+- 5. **Content-to-code ratio**: text should be more than code. Adsense flags pages at 60% code / 40% text - keep it the opposite. If code is long, link to GitHub and reference it in comments instead.
+
+- 6. Include a **bulleted summary section** at the top of the blog post.
+- 7. Include a **FAQ section** at the bottom.
+- 8. Use Docusaurus admonitions (`:::tip`, `:::info`, `:::note`) for callouts, tips, and cautions (see formatting guidelines below).
+- 9. Tables must be **center-aligned** - wrap them in an `:::info` block to achieve this in Docusaurus.
+- 10. Use **named code blocks** with a filename label when showing code (e.g., ` ```java title="Sample.java" `).
+- 11. When showing a query and its output together, use a **Tabs** block with separate "Query" and "Output" tabs.
+- 12. Screenshots must follow the naming convention and size guidelines below.
+:::
+
+---
+
## Step 4: Create the Blog Folder and File
All blog posts live inside the `blog/` directory. Each post gets its own folder.
@@ -149,7 +171,7 @@ After the closing `---` of your frontmatter, add the `` comment
...frontmatter...
---
-
+
Your introduction paragraph goes here. This will appear as the preview on the blog index page.
@@ -158,18 +180,168 @@ Your introduction paragraph goes here. This will appear as the preview on the bl
Body content continues here...
```
-### Formatting Tips
+### Formatting Guidelines
+
+Use `##` and `###` headings to structure your content.
+
+---
+
+#### Bulleted Summary Section (Required)
+
+Every blog must begin with a bulleted summary right after the intro paragraph. This helps readers quickly understand what they'll learn.
+
+```md
+**What you'll learn in this post:**
+- How to set up X from scratch
+- How to configure Y for production
+- Common pitfalls and how to avoid them
+```
+
+---
+
+#### Named Code Blocks (Required)
+
+Always label code blocks with a filename so readers know exactly what file they are editing:
+
+````md
+```java title="Sample.java"
+public class Hello {
+ public static void main(String[] args) {
+ System.out.println("Hello, world!");
+ }
+}
+```
+````
+
+---
+
+#### Query + Output: Use Tabs (Required)
-- Use `##` and `###` headings to structure your content.
-- Use fenced code blocks with the language name for syntax highlighting:
+When showing a database query alongside its output, use a Tabs block so both fit in a single window.
- ````md
- ```python
- print("Hello, world!")
+First, import the components at the top of your `index.md` (after frontmatter, before any content):
+
+```md
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+```
+
+Then structure your query + output like this:
+
+````md
+
+
+
+ ```sql
+ -- Create the table
+ CREATE TABLE friends (
+ id INT PRIMARY KEY,
+ name VARCHAR(100),
+ username VARCHAR(100)
+ );
+
+ -- Insert data
+ INSERT INTO friends (id, name, username) VALUES
+ (1, 'John Doe', 'johndoe'),
+ (2, 'Jane Smith', 'janesmith'),
+ (3, 'Bob Johnson', 'bobjohnson');
```
- ````
-- Use `>` for callout blockquotes and `---` for horizontal dividers between sections.
+
+
+
+ | id | name | username |
+ |----|-------------|-------------|
+ | 1 | John Doe | johndoe |
+ | 2 | Jane Smith | janesmith |
+ | 3 | Bob Johnson | bobjohnson |
+
+
+
+````
+
+:::tip
+You can add as many `` tabs as needed β for example separate tabs per subquery type, or one tab per language variant.
+:::
+
+---
+
+#### Admonitions: Tips, Notes, Info, and Cautions
+
+Use Docusaurus admonitions to highlight important information. Don't overuse them β only where they add real value.
+
+**For tips and helpful extras:**
+
+```md
+:::tip Need Git Commands?
+Check out our [comprehensive Git Commands Cheatsheet](../GitHub/setup-environment/git-commands.md)
+with 50 essential Git commands and examples.
+:::
+```
+
+**For extra context or caution:**
+
+```md
+:::info
+In the picture below, Developer 1 handles the men's shopping section, Developer 2
+deals with the women's section, and Developer 3 works on the login feature.
+:::
+```
+
+**For key feature callouts:**
+
+```md
+:::note
+Key Features of GitLab:
+- GitLab provides **built-in CI/CD pipelines**.
+- Unlike GitHub, GitLab can be **self-hosted** or used on the cloud (GitLab.com).
+- GitLab offers [Premium Plans](https://about.gitlab.com/pricing/) with advanced CI/CD and security features.
+:::
+```
+
+---
+
+#### Tables: Center Alignment via `:::info`
+
+Plain Markdown tables are left-aligned by default in Docusaurus. Wrap your table in an `:::info` block to center it:
+
+```md
+:::
+
+| Command | Description |
+|-------------|-------------------|
+| `git init` | Initialize a repo |
+| `git clone` | Clone a repo |
+
+:::
+```
+
+### Rendered Output
+
+:::info
+
+| Command | Description |
+|-------------|-------------------|
+| `git init` | Initialize a repo |
+| `git clone` | Clone a repo |
+
+:::
+
+---
+
+#### FAQ Section (Required)
+
+Every blog post must end with a FAQ section before the conclusion. Use questions your readers are likely to have:
+
+```md
+## Frequently Asked Questions
+
+**Q: Do I need to know X before starting this guide?**
+A: Basic familiarity with Y is helpful, but the guide covers everything step by step.
+
+**Q: Will this work on Windows?**
+A: Yes, the steps are cross-platform. Windows-specific commands are noted where they differ.
+```
---
@@ -188,7 +360,7 @@ Use **PNG** for UI screenshots (crisp text) and **JPEG/WebP** for photos.
### Naming Convention
-Use lowercase, hyphen-separated, numbered filenames so they sort correctly:
+Use lowercase, hyphen-separated, numbered filenames so they sort correctly and are SEO-friendly. **Never use random or auto-generated names.**
```
images/
@@ -208,20 +380,52 @@ Reference images relative to `index.md`:
Always write descriptive alt text β it improves accessibility and SEO.
+:::tip Screenshot Tool Recommendation
+Tools like [Snagit](https://www.techsmith.com/screen-capture.html) make it easy to produce annotated, professional-quality screenshots. See [this article](https://www.recodehive.com/docs/GitHub/Maintainer-guide/milestone) as a reference for the image quality standard we aim for.
+:::
+
+---
+
+## Step 9: Update the Database
+
+All blog data is linked in the database folder (`\database\blogs\index.tsx`). Update it with the following details:
+
+```json
+{
+ id: sequence_wise,
+ title: "Title of the post",
+ image: "relative path of the cover image for the blog post",
+ description: "A short (2-3) lines of description of the post",
+ slug: "The name of the blog folder (keep it exact)",
+ authors: ["your-author-id"],
+ category: "The category the blog belongs to",
+ tags: ["tags or topics the blog is related to (tools or technologies)"],
+}
+```
+
+:::note
+All details are necessary for correctly rendering the blog card on the blogs page. Take a close look and make sure everything is filled in.
+:::
+
---
-## Step 9: Preview Your Post
+## Step 10: Preview Your Post
Make sure your dev server is still running (`npm start`), then navigate to [http://localhost:3000/blog](http://localhost:3000/blog) to see your post in the listing and click into it to read the full content. Verify:
- The frontmatter title, date, and author show correctly.
- The truncate preview looks right on the blog index.
+- The bulleted summary section appears near the top.
- All images load and are properly sized.
-- Code blocks are syntax-highlighted.
+- Code blocks are syntax-highlighted and have filename labels.
+- Query/output pairs use Tabs.
+- Tables are center-aligned inside `:::info` blocks.
+- Tips and notes use the correct admonition type.
+- The FAQ section is present at the bottom.
---
-## Step 10: Commit and Push Your Changes
+## Step 11: Commit and Push Your Changes
Once you are happy with the preview, stage and commit your files:
@@ -239,13 +443,13 @@ git push origin blog/your-blog-title
---
-## Step 11: Open a Pull Request
+## Step 12: Open a Pull Request
-1. Go to your fork on GitHub β you will see a **"Compare & pull request"** banner. Click it.
-2. Set the **base repository** to `recodehive/recode-website` and **base branch** to `main`.
-3. Write a clear PR title, e.g. `blog: Add post on Your Blog Title`.
-4. In the description, briefly summarize what the post covers.
-5. Click **Create pull request**.
+- 1. Go to your fork on GitHub β you will see a **"Compare & pull request"** banner. Click it.
+- 2. Set the **base repository** to `recodehive/recode-website` and **base branch** to `main`.
+- 3. Write a clear PR title, e.g. `blog: Add post on Your Blog Title`.
+- 4. In the description, briefly summarize what the post covers.
+- 5. Click **Create pull request**.
A maintainer will review and merge your post. You may be asked to make small edits before it is approved.
@@ -268,13 +472,24 @@ git push origin main
Before submitting your PR, go through this checklist:
-- [ ] Blog folder created at `blog/your-blog-title/index.md`
-- [ ] Frontmatter is complete (title, authors, tags, date, description, draft: false)
-- [ ] Author entry exists in `blog/authors.yml`
-- [ ] `` comment placed after the intro paragraph
-- [ ] All images are in `blog/your-blog-title/images/` and named with the convention
-- [ ] Cover image is 1200 Γ 630 px; step screenshots are no wider than 1280 px
-- [ ] Image file sizes are under 500 KB each
-- [ ] Post previews correctly at `localhost:3000/blog`
-- [ ] Committed on a feature branch (not `main`)
-- [ ] Pull request targets `recodehive/recode-website` `main` branch
+- 1. [ ] Blog folder created at `blog/your-blog-title/index.md`
+- 2. [ ] Frontmatter is complete (title, authors, tags, date, description, draft: false)
+- 3. [ ] Author entry exists in `blog/authors.yml`
+- 4. [ ] `` comment placed after the intro paragraph
+- 5. [ ] **Bulleted summary section** included near the top of the post
+- 6. [ ] **FAQ section** included at the bottom of the post
+- 7. [ ] No generic content β article is high-depth and technical with images
+- 8. [ ] 5 external backlinks to supporting websites
+- 9. [ ] 5 internal backlinks to other recodehive articles
+- 10. [ ] Text is more than code β long code blocks link to GitHub instead
+- 11. [ ] Code blocks use filename labels β e.g., opening fence followed by `python title="app.py"`
+- 12. [ ] Query + output pairs use Tabs blocks
+- 14. [ ] Tables are wrapped in `:::info` for center alignment
+- 15. [ ] Tips, notes, and cautions use the correct Docusaurus admonition
+- 16. [ ] All images are in `blog/your-blog-title/images/` with SEO-friendly names
+- 17. [ ] Cover image is 1200 Γ 630 px; step screenshots are no wider than 1280 px
+- 18. [ ] Image file sizes are under 500 KB each
+- 19. [ ] Post previews correctly at `localhost:3000/blog`
+- 20. [ ] Database entry added in `\database\blogs\index.tsx`
+- 21. [ ] Committed on a feature branch (not `main`)
+- 22. [ ] Pull request targets `recodehive/recode-website` `main` branch
\ No newline at end of file
diff --git a/docs/image.png b/docs/image.png
new file mode 100644
index 00000000..57d0b044
Binary files /dev/null and b/docs/image.png differ
diff --git a/docs/sql/setup-environment.md b/docs/sql/setup-environment.md
index a95494bb..84c025cd 100644
--- a/docs/sql/setup-environment.md
+++ b/docs/sql/setup-environment.md
@@ -29,7 +29,7 @@ Click the link below to download MySQL Workbench:
- β
Choose your OS (Windows, macOS, or Linux)
- β
Follow the installation instructions
-
+
---
diff --git a/docusaurus.config.ts b/docusaurus.config.ts
index 40d02595..816bfae6 100644
--- a/docusaurus.config.ts
+++ b/docusaurus.config.ts
@@ -3,6 +3,7 @@ import type { Config } from "@docusaurus/types";
import type * as Preset from "@docusaurus/preset-classic";
import * as dotenv from "dotenv";
import giscusInjector from "./src/plugins/giscus-injector";
+dotenv.config({ path: ".env.local" });
dotenv.config();
// This runs in Node.js - Don't use client-side code here (browser APIs, JSX...)
@@ -114,6 +115,7 @@ const config: Config = {
type: "dropdown",
html: ' Docs',
position: "left",
+ to: "/docs/",
items: [
{
type: "html",
@@ -247,7 +249,7 @@ const config: Config = {
{
type: "html",
position: "right",
- value: '',
+ value: '',
},
],
},
@@ -286,6 +288,7 @@ const config: Config = {
// β
Add this customFields object to expose the token to the client-side
customFields: {
gitToken: process.env.DOCUSAURUS_GIT_TOKEN,
+ clerkPublishableKey: process.env.VITE_CLERK_PUBLISHABLE_KEY || "",
// Shopify credentials for merch store
SHOPIFY_STORE_DOMAIN:
process.env.SHOPIFY_STORE_DOMAIN || "junh9v-gw.myshopify.com",
diff --git a/package-lock.json b/package-lock.json
index ce3b812b..400d303b 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -8,6 +8,7 @@
"name": "recodehive",
"version": "0.0.0",
"dependencies": {
+ "@clerk/react": "^6.6.6",
"@docusaurus/core": "^3.9.1",
"@docusaurus/plugin-content-docs": "3.10.1",
"@docusaurus/plugin-google-analytics": "^3.10.0",
@@ -26,17 +27,15 @@
"@radix-ui/react-collapsible": "^1.1.12",
"@radix-ui/react-slot": "^1.2.3",
"@tsparticles/react": "^3.0.0",
- "@tsparticles/slim": "^3.8.1",
+ "@tsparticles/slim": "^4.0.4",
"@vercel/analytics": "^1.5.0",
"canvas-confetti": "^1.9.3",
"class-variance-authority": "^0.7.1",
"clsx": "^2.1.1",
- "date-fns": "^4.1.0",
+ "date-fns": "^4.2.1",
"dotenv": "^17.4.2",
"embla-carousel-autoplay": "^8.6.0",
"embla-carousel-react": "^8.6.0",
- "firebase": "^9.22.2",
- "firebaseui": "6.1.0",
"framer-motion": "^12.38.0",
"lucide-react": "^0.503.0",
"prism-react-renderer": "^2.3.0",
@@ -45,6 +44,7 @@
"react-icons": "^5.5.0",
"react-slot-counter": "^3.3.1",
"rehype-katex": "^7.0.1",
+ "remark-gfm": "^4.0.1",
"remark-math": "^6.0.0",
"styled-components": "^6.4.1",
"tailwind-merge": "^3.6.0",
@@ -59,7 +59,7 @@
"@types/canvas-confetti": "^1.9.0",
"@types/react": "^19.1.9",
"@types/react-dom": "^19.1.7",
- "@typescript-eslint/eslint-plugin": "^8.59.3",
+ "@typescript-eslint/eslint-plugin": "^8.59.4",
"@typescript-eslint/parser": "^8.59.3",
"autoprefixer": "^10.5.0",
"eslint": "^9.38.0",
@@ -73,7 +73,7 @@
"typescript": "~5.3"
},
"engines": {
- "node": ">=18.0"
+ "node": ">=20.9.0"
}
},
"node_modules/@algolia/abtesting": {
@@ -2089,6 +2089,52 @@
"integrity": "sha512-YslZMgtJUyuMbZ+aKvfF3x1f5liK4mWNxghFRv7jqRR9C3R3fAOGTTKvxXDa2Y1s9zSbcpuO0cAxDYsc9SrXoQ==",
"license": "Apache-2.0"
},
+ "node_modules/@clerk/react": {
+ "version": "6.6.6",
+ "resolved": "https://registry.npmjs.org/@clerk/react/-/react-6.6.6.tgz",
+ "integrity": "sha512-MVHLDZeGobSbGSZgAdb4G1BbB8ZU5XAmBBdIVLXiPOLEst2TYM5bP137WRA76y9GlmVmKYdKzph/TUi87P/JOA==",
+ "license": "MIT",
+ "dependencies": {
+ "@clerk/shared": "^4.12.2",
+ "tslib": "2.8.1"
+ },
+ "engines": {
+ "node": ">=20.9.0"
+ },
+ "peerDependencies": {
+ "react": "^18.0.0 || ~19.0.3 || ~19.1.4 || ~19.2.3 || ~19.3.0-0",
+ "react-dom": "^18.0.0 || ~19.0.3 || ~19.1.4 || ~19.2.3 || ~19.3.0-0"
+ }
+ },
+ "node_modules/@clerk/shared": {
+ "version": "4.12.2",
+ "resolved": "https://registry.npmjs.org/@clerk/shared/-/shared-4.12.2.tgz",
+ "integrity": "sha512-jDkip8tKTzYz/cPKMCsjOoACH3Xh37zcbCrssMRTYOq3GZypIpZ6WAs4m4G82URL0WY+yz5frrHVjRrHyAb6LA==",
+ "hasInstallScript": true,
+ "license": "MIT",
+ "dependencies": {
+ "@tanstack/query-core": "^5.100.6",
+ "dequal": "2.0.3",
+ "glob-to-regexp": "0.4.1",
+ "js-cookie": "3.0.5",
+ "std-env": "^3.9.0"
+ },
+ "engines": {
+ "node": ">=20.9.0"
+ },
+ "peerDependencies": {
+ "react": "^18.0.0 || ~19.0.3 || ~19.1.4 || ~19.2.3 || ~19.3.0-0",
+ "react-dom": "^18.0.0 || ~19.0.3 || ~19.1.4 || ~19.2.3 || ~19.3.0-0"
+ },
+ "peerDependenciesMeta": {
+ "react": {
+ "optional": true
+ },
+ "react-dom": {
+ "optional": true
+ }
+ }
+ },
"node_modules/@colors/colors": {
"version": "1.5.0",
"resolved": "https://registry.npmjs.org/@colors/colors/-/colors-1.5.0.tgz",
@@ -6487,538 +6533,6 @@
"node": "^18.18.0 || ^20.9.0 || >=21.1.0"
}
},
- "node_modules/@firebase/analytics": {
- "version": "0.10.0",
- "resolved": "https://registry.npmjs.org/@firebase/analytics/-/analytics-0.10.0.tgz",
- "integrity": "sha512-Locv8gAqx0e+GX/0SI3dzmBY5e9kjVDtD+3zCFLJ0tH2hJwuCAiL+5WkHuxKj92rqQj/rvkBUCfA1ewlX2hehg==",
- "license": "Apache-2.0",
- "dependencies": {
- "@firebase/component": "0.6.4",
- "@firebase/installations": "0.6.4",
- "@firebase/logger": "0.4.0",
- "@firebase/util": "1.9.3",
- "tslib": "^2.1.0"
- },
- "peerDependencies": {
- "@firebase/app": "0.x"
- }
- },
- "node_modules/@firebase/analytics-compat": {
- "version": "0.2.6",
- "resolved": "https://registry.npmjs.org/@firebase/analytics-compat/-/analytics-compat-0.2.6.tgz",
- "integrity": "sha512-4MqpVLFkGK7NJf/5wPEEP7ePBJatwYpyjgJ+wQHQGHfzaCDgntOnl9rL2vbVGGKCnRqWtZDIWhctB86UWXaX2Q==",
- "license": "Apache-2.0",
- "dependencies": {
- "@firebase/analytics": "0.10.0",
- "@firebase/analytics-types": "0.8.0",
- "@firebase/component": "0.6.4",
- "@firebase/util": "1.9.3",
- "tslib": "^2.1.0"
- },
- "peerDependencies": {
- "@firebase/app-compat": "0.x"
- }
- },
- "node_modules/@firebase/analytics-types": {
- "version": "0.8.0",
- "resolved": "https://registry.npmjs.org/@firebase/analytics-types/-/analytics-types-0.8.0.tgz",
- "integrity": "sha512-iRP+QKI2+oz3UAh4nPEq14CsEjrjD6a5+fuypjScisAh9kXKFvdJOZJDwk7kikLvWVLGEs9+kIUS4LPQV7VZVw==",
- "license": "Apache-2.0"
- },
- "node_modules/@firebase/app": {
- "version": "0.9.13",
- "resolved": "https://registry.npmjs.org/@firebase/app/-/app-0.9.13.tgz",
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"url": "https://www.buymeacoffee.com/matteobruni"
}
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@@ -11864,63 +11265,6 @@
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@@ -27480,12 +26683,6 @@
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@@ -28207,15 +27394,6 @@
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@@ -28239,62 +27417,6 @@
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diff --git a/package.json b/package.json
index 2355cd61..5ee3e98e 100644
--- a/package.json
+++ b/package.json
@@ -27,6 +27,7 @@
]
},
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@@ -45,17 +46,15 @@
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+ "@tsparticles/slim": "^4.0.4",
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"canvas-confetti": "^1.9.3",
"class-variance-authority": "^0.7.1",
"clsx": "^2.1.1",
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"embla-carousel-react": "^8.6.0",
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- "firebaseui": "6.1.0",
"framer-motion": "^12.38.0",
"lucide-react": "^0.503.0",
"prism-react-renderer": "^2.3.0",
@@ -64,6 +63,7 @@
"react-icons": "^5.5.0",
"react-slot-counter": "^3.3.1",
"rehype-katex": "^7.0.1",
+ "remark-gfm": "^4.0.1",
"remark-math": "^6.0.0",
"styled-components": "^6.4.1",
"tailwind-merge": "^3.6.0",
@@ -78,7 +78,7 @@
"@types/canvas-confetti": "^1.9.0",
"@types/react": "^19.1.9",
"@types/react-dom": "^19.1.7",
- "@typescript-eslint/eslint-plugin": "^8.59.3",
+ "@typescript-eslint/eslint-plugin": "^8.59.4",
"@typescript-eslint/parser": "^8.59.3",
"autoprefixer": "^10.5.0",
"eslint": "^9.38.0",
@@ -104,6 +104,6 @@
]
},
"engines": {
- "node": ">=18.0"
+ "node": ">=20.9.0"
}
}
diff --git a/sidebars.ts b/sidebars.ts
index 5329ca0f..9867fd59 100644
--- a/sidebars.ts
+++ b/sidebars.ts
@@ -33,7 +33,7 @@ const sidebars: SidebarsConfig = {
className: "custom-sidebar-github",
items: [
"GitHub/intro-github",
- "GitHub/intro-gitlab",
+
{
type: "category",
label: "βοΈ Setup Environment",
diff --git a/src/components/FloatingContributors/index.tsx b/src/components/FloatingContributors/index.tsx
index 171af61f..bcfcedab 100644
--- a/src/components/FloatingContributors/index.tsx
+++ b/src/components/FloatingContributors/index.tsx
@@ -87,13 +87,13 @@ interface ContributorActivity {
html_url: string;
};
action:
- | "pushed"
- | "created"
- | "merged"
- | "opened"
- | "commented"
- | "closed"
- | "other";
+ | "pushed"
+ | "created"
+ | "merged"
+ | "opened"
+ | "commented"
+ | "closed"
+ | "other";
message?: string;
timestamp: Date;
timeAgo: string;
@@ -519,17 +519,17 @@ const FloatingContributors: React.FC = ({
headerEmbedded
? {}
: {
- y: [0, -8, 0],
- }
+ y: [0, -8, 0],
+ }
}
transition={
headerEmbedded
? {}
: {
- duration: 4,
- repeat: Infinity,
- ease: "easeInOut",
- }
+ duration: 4,
+ repeat: Infinity,
+ ease: "easeInOut",
+ }
}
>
{/* Close button */}
@@ -668,7 +668,7 @@ const FloatingContributors: React.FC = ({
{/* Footer */}
= ({
aria-label="View repository on GitHub and join the community"
>
π
- View Repository on GitHub
+ View Repositories on GitHub
β
diff --git a/src/components/blogCarousel/blogCard.tsx b/src/components/blogCarousel/blogCard.tsx
index 562fa5c9..480544d9 100644
--- a/src/components/blogCarousel/blogCard.tsx
+++ b/src/components/blogCarousel/blogCard.tsx
@@ -1,13 +1,27 @@
"use client";
-import * as React from "react";
-import { useState } from "react";
-import { motion } from "framer-motion";
+import React from "react";
import Link from "@docusaurus/Link";
-import { Card, CardContent } from "../ui/card";
-import { getAuthorNames } from "../../utils/authors";
+import { getAuthorProfiles, getAuthorTooltip } from "../../utils/authors";
-const BlogCard = ({ type, date, title, content, imageUrl, id, authors }) => {
- const [isHovered, setIsHovered] = useState(false);
+interface BlogCardProps {
+ type: string;
+ date?: string;
+ title: string;
+ content: string;
+ imageUrl: string;
+ id: string;
+ authors?: string[];
+}
+
+const BlogCard = ({
+ type,
+ title,
+ content,
+ imageUrl,
+ id,
+ authors,
+}: BlogCardProps) => {
+ const authorProfiles = getAuthorProfiles(authors || []);
if (!id || !type) {
return setIsHovered(true)}
- onMouseLeave={() => setIsHovered(false)}
- className="relative h-full overflow-hidden transition-all duration-300"
- >
-
-
# Introduction to Docker
diff --git a/docs/GitHub/GitHub-basics/create-github-repo.md b/docs/GitHub/GitHub-basics/create-github-repo.md
index 38065fc3..47221393 100644
--- a/docs/GitHub/GitHub-basics/create-github-repo.md
+++ b/docs/GitHub/GitHub-basics/create-github-repo.md
@@ -132,7 +132,7 @@ You will now be able to see the uploaded resume in your repository.
### Watch the video Tutorial
-
+
## Conclusion
diff --git a/docs/GitHub/GitHub-basics/first-opensource-code.md b/docs/GitHub/GitHub-basics/first-opensource-code.md
index 3cd186ea..836f8b2d 100644
--- a/docs/GitHub/GitHub-basics/first-opensource-code.md
+++ b/docs/GitHub/GitHub-basics/first-opensource-code.md
@@ -49,7 +49,7 @@ This is a beginner-friendly repository designed for practicing open source contr
Step 1: Repository Home Page
+ Step 1: Repository Home Page
Step 2: Forking the Repository
+ Step 2: Forking the Repository
Step 3: Fork Button Location
+ Step 3: Fork Button Location
Step 4: Fork Complete
+ Step 4: Fork Complete
Step 5: Add Your Name
+ Step 5: Add Your Name
Step 6: Changes Made
+ Step 6: Changes Made
Step 7: Commit Changes
+ Step 7: Commit Changes
Step 8: Final Commit Review
+ Step 8: Final Commit Review
Step 9: Create Pull Request
+ Step 9: Create Pull Request
data not fetched properly, imageId and entryId not found
;
@@ -36,58 +50,100 @@ const BlogCard = ({ type, date, title, content, imageUrl, id, authors }) => {
const category = getCategory(title);
return (
-
- {/* Category Badge */}
-
{category}
+
+
+ {/* Category Badge */}
+
{category}
- {/* Card Image */}
-
- 
-
+ {/* Card Image */}
+
+
+
- {/* Card Content */}
-
- = ({
- OurProjectsData: legacyData,
+const ShowcasePage: React.FC = ({
+ Data: legacyData,
}) => {
const { colorMode, isDark } = useSafeColorMode();
@@ -211,7 +210,7 @@ const SelectComponent = ({
{title}
-{content}
+ {/* Card Content */} +
+
);
};
diff --git a/src/components/blogCarousel/blogCarousel.css b/src/components/blogCarousel/blogCarousel.css
index bc9923a0..8495cb6d 100644
--- a/src/components/blogCarousel/blogCarousel.css
+++ b/src/components/blogCarousel/blogCarousel.css
@@ -179,6 +179,20 @@
color: #a78bfa;
}
+.card-title-link {
+ color: inherit;
+ text-decoration: none;
+ transition: color 0.2s ease;
+}
+
+.card-title-link:hover {
+ color: #6366f1;
+}
+
+[data-theme="dark"] .card-title-link:hover {
+ color: #a78bfa;
+}
+
.card-description {
font-size: 14px;
color: #64748b;
@@ -237,6 +251,125 @@
min-width: 0;
}
+.author-avatar-wrapper {
+ position: relative;
+ width: 28px;
+ height: 28px;
+ flex-shrink: 0;
+}
+/*
+.author-avatar-image {
+ width: 100%;
+ height: 100%;
+ border-radius: 50%;
+ object-fit: cover;
+ box-shadow: 0 2px 8px rgba(99, 102, 241, 0.3);
+ transition: all 0.3s ease;
+ border: 1px solid rgba(99, 102, 241, 0.2);
+}
+
+.author-avatar-image:hover {
+ transform: scale(1.1);
+ box-shadow: 0 4px 12px rgba(99, 102, 241, 0.4);
+}
+
+.author-avatar-fallback {
+ position: absolute;
+ top: 0;
+ left: 0;
+ width: 100%;
+ height: 100%;
+ border-radius: 50%;
+ background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%);
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ font-size: 12px;
+ color: white;
+ font-weight: 600;
+ box-shadow: 0 2px 8px rgba(99, 102, 241, 0.3);
+ transition: all 0.3s ease;
+}
+
+.author-avatar-fallback:hover {
+ transform: scale(1.1);
+ box-shadow: 0 4px 12px rgba(99, 102, 241, 0.4);
+} */
+
+.author-name-group {
+ display: flex;
+ align-items: center;
+ flex-wrap: wrap;
+ gap: 6px;
+ min-width: 0;
+}
+
+/* Stacked avatars */
+.author-stack {
+ display: flex;
+ align-items: center;
+ margin-right: 8px;
+}
+
+.author-stack-item {
+ width: 28px;
+ height: 28px;
+ border-radius: 50%;
+ overflow: hidden;
+ margin-left: -10px;
+ display: inline-flex;
+ align-items: center;
+ justify-content: center;
+ background: #fff;
+ border: 2px solid #fff;
+}
+
+.author-stack-avatar {
+ width: 100%;
+ height: 100%;
+ object-fit: cover;
+ display: block;
+}
+
+.author-stack-fallback {
+ width: 100%;
+ height: 100%;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%);
+ color: #fff;
+ font-weight: 700;
+}
+
+.author-stack-more {
+ width: 28px;
+ height: 28px;
+ border-radius: 50%;
+ background: #6366f1;
+ color: #fff;
+ display: inline-flex;
+ align-items: center;
+ justify-content: center;
+ margin-left: -10px;
+ font-size: 12px;
+ font-weight: 700;
+ border: 2px solid #fff;
+}
+
+.author-separator {
+ color: #94a3b8;
+ font-size: 12px;
+ font-weight: 700;
+}
+
+.author-link {
+ display: inline-flex;
+ align-items: center;
+ position: relative;
+ text-decoration: none;
+}
+
.author-avatar {
width: 28px;
height: 28px;
@@ -272,6 +405,58 @@
color: #6366f1;
}
+.author-name::after {
+ content: attr(data-author-tooltip);
+ position: absolute;
+ bottom: 100%;
+ left: 50%;
+ transform: translateX(-50%);
+ background: rgba(0, 0, 0, 0.9);
+ color: white;
+ padding: 8px 12px;
+ border-radius: 6px;
+ font-size: 12px;
+ white-space: pre-line;
+ max-width: 240px;
+ text-align: center;
+ opacity: 0;
+ visibility: hidden;
+ transition: all 0.3s ease;
+ z-index: 1000;
+ pointer-events: none;
+ margin-bottom: 5px;
+}
+
+.author-name::before {
+ content: "";
+ position: absolute;
+ bottom: 100%;
+ left: 50%;
+ transform: translateX(-50%);
+ border: 5px solid transparent;
+ border-top-color: rgba(0, 0, 0, 0.9);
+ opacity: 0;
+ visibility: hidden;
+ transition: all 0.3s ease;
+ z-index: 1000;
+ pointer-events: none;
+}
+
+[data-theme="dark"] .author-name::after {
+ background: rgba(18, 18, 18, 0.95);
+ color: #f7fafc;
+}
+
+[data-theme="dark"] .author-name::before {
+ border-top-color: rgba(18, 18, 18, 0.95);
+}
+
+.author-name:hover::after,
+.author-name:hover::before {
+ opacity: 1;
+ visibility: visible;
+}
+
[data-theme="dark"] .author-name {
color: #cbd5e1;
}
diff --git a/src/components/discussions/DiscussionCard.tsx b/src/components/discussions/DiscussionCard.tsx
index 58983939..be1c3692 100644
--- a/src/components/discussions/DiscussionCard.tsx
+++ b/src/components/discussions/DiscussionCard.tsx
@@ -1,6 +1,6 @@
import React from "react";
import { motion } from "framer-motion";
-import { MessageCircle, ThumbsUp, Calendar, Tag, User } from "lucide-react";
+import { MessageCircle, ThumbsUp, Calendar, Tag } from "lucide-react";
export interface Discussion {
id: string;
@@ -129,7 +129,8 @@ export default function DiscussionCard({
onError={(e) => {
const target = e.currentTarget;
target.style.display = "none";
- const fallback = target.nextElementSibling;
+ const fallback =
+ target.nextElementSibling as HTMLElement | null;
if (fallback) fallback.style.display = "flex";
}}
/>
diff --git a/src/components/ourProjects.tsx b/src/components/ourProjects.tsx
index a06078ee..4ccc41b0 100644
--- a/src/components/ourProjects.tsx
+++ b/src/components/ourProjects.tsx
@@ -6,8 +6,7 @@ import { motion } from "framer-motion";
import React from "react";
import { useSafeColorMode } from "../utils/useSafeColorMode";
// Mobile-specific overrides for very small screens (<768px and <320px)
-import "./ourProjects.mobile.css";
-// Import projects data and types
+import "./ourProjects.mobile.css";// Import projects data and types
import projectsData from "../data/projects.json";
import type { ProjectsData, ProjectItem } from "../data/types";
@@ -15,7 +14,7 @@ import type { ProjectsData, ProjectItem } from "../data/types";
* Legacy interface for backward compatibility
* @deprecated Use ProjectsData from types.ts instead
*/
-export interface OurProjectsData {
+export interface Data {
tag: string;
title: string;
description: string;
@@ -29,12 +28,12 @@ export interface OurProjectsData {
* Legacy props interface for backward compatibility
* @deprecated Component now imports data directly
*/
-export interface OurProjectsProps {
- OurProjectsData?: OurProjectsData;
+export interface Props {
+ Data?: Data;
}
/**
- * OurProjects Component
+ * Component
*
* Displays a showcase of RecodeHive projects with interactive previews.
* Now uses data-driven approach with JSON configuration for better maintainability.
@@ -48,8 +47,8 @@ export interface OurProjectsProps {
*
* @param props - Optional props for backward compatibility
*/
-const OurProjects: React.FC+ + {title} + +
+{content}
- {/* Card Meta */} -
-
+
- π€
-
- {getAuthorNames(authors || [])}
-
+ {/* Card Meta */}
+
-
-
+
+
+
+ {/* Read More Button */}
+
+ Read Article β
+
+ {/* Stacked Author Avatars */}
+ {authorProfiles.length > 0 &&
+ (() => {
+ const max = 3;
+ const visible = authorProfiles.slice(0, max);
+ const extra = Math.max(0, authorProfiles.length - max);
+ return (
+
-
- {/* Read More Button */}
-
+ {visible.map((a, i) => (
+
+ );
+ })()}
+
+ {/* Author Names */}
+
+ {a.imageUrl ? (
+ 
{
+ const target = e.currentTarget;
+ target.style.display = "none";
+ const fallback =
+ target.nextElementSibling as HTMLElement | null;
+ if (fallback) fallback.style.display = "flex";
+ }}
+ />
+ ) : (
+
+ {a.name.charAt(0).toUpperCase()}
+
+ )}
+
+ ))}
+ {extra > 0 && (
+ +{extra}
+ )}
+
+ {authorProfiles.map((author, authorIndex) => (
+
+ {authorIndex > 0 && (
+ &
+ )}
+
+ {author.name}
+
+
+ ))}
- 5 min read
Read Article β
+ 5 min read
{/* Animated Mesh Background */}
@@ -288,7 +287,7 @@ const SelectComponent = ({
className="perspective-1000 relative z-10"
>
) => {
- setSearchTerm(e.target.value);
+ const handleSearchChange = (e: ChangeEvent) => {
+ setSearchInput(e.target.value);
};
- const handleCategoryClick = (category: string) => {
- setSelectedCategory(category);
+ const handleSearchSubmit = (e: React.FormEvent) => {
+ e.preventDefault();
+ setSearchTerm(searchInput.trim());
};
const handleClearFilters = () => {
+ setSearchInput("");
setSearchTerm("");
- setSelectedCategory("All");
};
return (
@@ -125,119 +104,66 @@ export default function Blogs(): React.JSX.Element {
{/* Latest Articles Section */}
{/* Holographic Overlay */}
-
diff --git a/src/pages/blogs/blogs-new.css b/src/pages/blogs/blogs-new.css
index bc430621..2867b571 100644
--- a/src/pages/blogs/blogs-new.css
+++ b/src/pages/blogs/blogs-new.css
@@ -655,6 +655,7 @@
max-width: 1400px;
margin: 0 auto;
display: flex;
+ flex-direction: column;
gap: 40px;
position: relative;
z-index: 1;
@@ -936,6 +937,182 @@
display: none;
}
+.blog-search-panel {
+ width: min(100%, 980px);
+ margin: 0 auto 8px;
+ padding: 36px;
+ text-align: center;
+ background:
+ linear-gradient(135deg, rgba(255, 255, 255, 0.95), rgba(248, 250, 252, 0.9)),
+ radial-gradient(circle at 15% 20%, rgba(99, 102, 241, 0.12), transparent 36%),
+ radial-gradient(circle at 85% 0%, rgba(236, 72, 153, 0.1), transparent 34%);
+ border: 1px solid rgba(99, 102, 241, 0.12);
+ border-radius: 24px;
+ box-shadow:
+ 0 24px 70px rgba(15, 23, 42, 0.08),
+ 0 10px 30px rgba(99, 102, 241, 0.12);
+}
+
+[data-theme="dark"] .blog-search-panel {
+ background:
+ linear-gradient(135deg, rgba(36, 37, 38, 0.96), rgba(26, 32, 44, 0.94)),
+ radial-gradient(circle at 15% 20%, rgba(167, 139, 250, 0.16), transparent 36%),
+ radial-gradient(circle at 85% 0%, rgba(236, 72, 153, 0.1), transparent 34%);
+ border-color: rgba(167, 139, 250, 0.2);
+ box-shadow:
+ 0 24px 70px rgba(0, 0, 0, 0.38),
+ 0 10px 30px rgba(99, 102, 241, 0.18);
+}
+
+.blog-search-eyebrow {
+ margin: 0 0 8px;
+ color: #6366f1;
+ font-size: 13px;
+ font-weight: 800;
+ letter-spacing: 0.12em;
+ text-transform: uppercase;
+}
+
+[data-theme="dark"] .blog-search-eyebrow {
+ color: #a78bfa;
+}
+
+.blog-search-title {
+ margin: 0 0 24px;
+ color: #0f172a;
+ font-size: 34px;
+ font-weight: 850;
+ line-height: 1.15;
+}
+
+[data-theme="dark"] .blog-search-title {
+ color: #f8fafc;
+}
+
+.blog-search-form {
+ display: grid;
+ grid-template-columns: minmax(0, 1fr) auto auto;
+ gap: 12px;
+ align-items: center;
+}
+
+.blog-search-field {
+ position: relative;
+ display: flex;
+ align-items: center;
+ min-width: 0;
+}
+
+.blog-search-field input {
+ width: 100%;
+ min-height: 56px;
+ padding: 0 18px 0 52px;
+ color: #0f172a;
+ background: #ffffff;
+ border: 2px solid rgba(99, 102, 241, 0.18);
+ border-radius: 14px;
+ font-size: 16px;
+ font-weight: 500;
+ outline: none;
+ transition:
+ border-color 0.2s ease,
+ box-shadow 0.2s ease,
+ transform 0.2s ease;
+}
+
+.blog-search-field input:focus {
+ border-color: #6366f1;
+ box-shadow: 0 0 0 4px rgba(99, 102, 241, 0.12);
+ transform: translateY(-1px);
+}
+
+.blog-search-field input::placeholder {
+ color: #94a3b8;
+}
+
+[data-theme="dark"] .blog-search-field input {
+ color: #f8fafc;
+ background: rgba(15, 23, 42, 0.82);
+ border-color: rgba(167, 139, 250, 0.24);
+}
+
+[data-theme="dark"] .blog-search-field input:focus {
+ border-color: #a78bfa;
+ box-shadow: 0 0 0 4px rgba(167, 139, 250, 0.14);
+}
+
+.blog-search-submit-icon {
+ position: absolute;
+ left: 18px;
+ width: 20px;
+ height: 20px;
+ color: #6366f1;
+ pointer-events: none;
+}
+
+[data-theme="dark"] .blog-search-submit-icon {
+ color: #a78bfa;
+}
+
+.blog-search-button,
+.blog-search-clear-button {
+ min-height: 56px;
+ padding: 0 26px;
+ border: none;
+ border-radius: 14px;
+ font-size: 15px;
+ font-weight: 800;
+ cursor: pointer;
+ transition:
+ transform 0.2s ease,
+ box-shadow 0.2s ease,
+ background 0.2s ease;
+}
+
+.blog-search-button {
+ color: #ffffff;
+ background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%);
+ box-shadow: 0 12px 28px rgba(99, 102, 241, 0.28);
+}
+
+.blog-search-button:hover {
+ transform: translateY(-2px);
+ box-shadow: 0 16px 34px rgba(99, 102, 241, 0.36);
+}
+
+.blog-search-clear-button {
+ color: #475569;
+ background: rgba(226, 232, 240, 0.74);
+}
+
+.blog-search-clear-button:hover {
+ color: #0f172a;
+ background: #e2e8f0;
+ transform: translateY(-2px);
+}
+
+[data-theme="dark"] .blog-search-clear-button {
+ color: #e2e8f0;
+ background: rgba(71, 85, 105, 0.54);
+}
+
+[data-theme="dark"] .blog-search-clear-button:hover {
+ color: #ffffff;
+ background: rgba(100, 116, 139, 0.7);
+}
+
+.blog-search-visually-hidden {
+ position: absolute;
+ width: 1px;
+ height: 1px;
+ padding: 0;
+ margin: -1px;
+ overflow: hidden;
+ clip: rect(0, 0, 0, 0);
+ white-space: nowrap;
+ border: 0;
+}
+
/* Search Results Info */
.search-results-info {
text-align: center;
@@ -1270,6 +1447,7 @@
.articles-main-content {
flex: 1;
min-width: 0;
+ width: 100%;
}
/* Articles Grid */
@@ -1590,6 +1768,20 @@
margin-bottom: 12px;
}
+
+ .card-title-link {
+ color: inherit;
+ text-decoration: none;
+ transition: color 0.2s ease;
+ }
+
+ .card-title-link:hover {
+ color: #6366f1;
+ }
+
+ [data-theme="dark"] .card-title-link:hover {
+ color: #a78bfa;
+ }
.card-description {
font-size: 14px;
margin-bottom: 20px;
@@ -1635,6 +1827,78 @@
min-width: 0; /* Allow text to shrink */
}
+.author-name-group {
+ display: flex;
+ align-items: center;
+ flex-wrap: wrap;
+ gap: 6px;
+ min-width: 0;
+}
+
+.author-item {
+ display: inline-flex;
+ align-items: center;
+ gap: 6px;
+}
+
+.author-separator {
+ color: #94a3b8;
+ font-size: 13px;
+ font-weight: 700;
+}
+
+.author-link {
+ display: inline-flex;
+ align-items: center;
+ position: relative;
+ text-decoration: none;
+}
+
+/* .author-avatar-wrapper {
+ position: relative;
+ width: 32px;
+ height: 32px;
+ flex-shrink: 0;
+}
+
+.author-avatar-image {
+ width: 100%;
+ height: 100%;
+ border-radius: 50%;
+ object-fit: cover;
+ box-shadow: 0 2px 8px rgba(99, 102, 241, 0.3);
+ transition: all 0.3s ease;
+ border: 1px solid rgba(99, 102, 241, 0.2);
+}
+
+.author-avatar-image:hover {
+ transform: scale(1.1);
+ box-shadow: 0 4px 12px rgba(99, 102, 241, 0.4);
+}
+
+.author-avatar-fallback {
+ position: absolute;
+ top: 0;
+ left: 0;
+ width: 100%;
+ height: 100%;
+ border-radius: 50%;
+ background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%);
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ font-size: 14px;
+ color: white;
+ font-weight: 600;
+ box-shadow: 0 2px 8px rgba(99, 102, 241, 0.3);
+ transition: all 0.3s ease;
+} */
+
+.author-avatar-fallback:hover {
+ transform: scale(1.1);
+ box-shadow: 0 4px 12px rgba(99, 102, 241, 0.4);
+}
+
.author-avatar {
width: 32px;
height: 32px;
@@ -1651,6 +1915,59 @@
transition: all 0.3s ease;
}
+/* Stacked avatars */
+.author-stack {
+ display: flex;
+ align-items: center;
+ margin-right: 8px;
+}
+
+.author-stack-item {
+ width: 32px;
+ height: 32px;
+ border-radius: 50%;
+ overflow: hidden;
+ margin-left: -10px;
+ display: inline-flex;
+ align-items: center;
+ justify-content: center;
+ background: #fff;
+ border: 2px solid #fff;
+}
+
+.author-stack-avatar {
+ width: 100%;
+ height: 100%;
+ object-fit: cover;
+ display: block;
+}
+
+.author-stack-fallback {
+ width: 100%;
+ height: 100%;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%);
+ color: #fff;
+ font-weight: 700;
+}
+
+.author-stack-more {
+ width: 32px;
+ height: 32px;
+ border-radius: 50%;
+ background: #6366f1;
+ color: #fff;
+ display: inline-flex;
+ align-items: center;
+ justify-content: center;
+ margin-left: -10px;
+ font-size: 12px;
+ font-weight: 700;
+ border: 2px solid #fff;
+}
+
.author-avatar:hover {
transform: scale(1.1);
box-shadow: 0 4px 12px rgba(99, 102, 241, 0.4);
@@ -1683,7 +2000,7 @@
/* Tooltip for author names */
.author-name::after {
- content: attr(data-full-name);
+ content: attr(data-author-tooltip);
position: absolute;
bottom: 100%;
left: 50%;
@@ -1693,7 +2010,9 @@
padding: 8px 12px;
border-radius: 6px;
font-size: 12px;
- white-space: nowrap;
+ white-space: pre-line;
+ max-width: 240px;
+ text-align: center;
opacity: 0;
visibility: hidden;
transition: all 0.3s ease;
@@ -1957,6 +2276,23 @@
.latest-articles-section {
padding: 60px 16px;
}
+
+ .blog-search-panel {
+ padding: 28px;
+ }
+
+ .blog-search-title {
+ font-size: 28px;
+ }
+
+ .blog-search-form {
+ grid-template-columns: 1fr;
+ }
+
+ .blog-search-button,
+ .blog-search-clear-button {
+ width: 100%;
+ }
}
/* Mobile Small */
@@ -2023,6 +2359,32 @@
margin-top: 28px;
}
+ .blog-search-panel {
+ padding: 24px 18px;
+ border-radius: 18px;
+ }
+
+ .blog-search-title {
+ font-size: 24px;
+ margin-bottom: 18px;
+ }
+
+ .blog-search-form {
+ grid-template-columns: 1fr;
+ gap: 10px;
+ }
+
+ .blog-search-field input,
+ .blog-search-button,
+ .blog-search-clear-button {
+ min-height: 50px;
+ }
+
+ .blog-search-button,
+ .blog-search-clear-button {
+ width: 100%;
+ }
+
.search-wrapper {
padding: 14px 16px;
}
diff --git a/src/pages/blogs/index.tsx b/src/pages/blogs/index.tsx
index e48fe178..f0fc8637 100644
--- a/src/pages/blogs/index.tsx
+++ b/src/pages/blogs/index.tsx
@@ -1,71 +1,50 @@
-import React, { useState, useEffect } from "react";
+import React, { type ChangeEvent } from "react";
import useDocusaurusContext from "@docusaurus/useDocusaurusContext";
import Layout from "@theme/Layout";
import Link from "@docusaurus/Link";
import blogs from "../../database/blogs/index";
import Head from "@docusaurus/Head";
+import { getAuthorProfiles, getAuthorTooltip } from "../../utils/authors";
import "./blogs-new.css";
-// Author mapping based on actual blog posts
-const authorMapping = {
- "streamline-ux-ui": ["Sowmiya Venketashan", "Sanjay Viswanthan"],
- "ux-ui-design-job": ["Sowmiya Venketashan", "Sanjay Viswanthan"],
- "ux-designers-ai": ["Sowmiya Venketashan", "Sanjay Viswanthan"],
- "git-coding-agent": ["Sanjay Viswanthan"],
- "spark-architecture": ["Aditya Singh Rathore", "Sanjay Viswanthan"],
- "n8n-workflow-automation": ["Aditya Singh Rathore"],
-};
-
-// Get unique categories from blogs
-const getUniqueCategories = () => {
- const categories = blogs.map((blog) => blog.category);
- return Array.from(new Set(categories)).sort();
-};
-
-export default function Blogs(): React.JSX.Element {
+export default function Blogs() {
const { siteConfig } = useDocusaurusContext();
- const [searchTerm, setSearchTerm] = useState("");
- const [selectedCategory, setSelectedCategory] = useState("All");
- const [filteredBlogs, setFilteredBlogs] = useState(blogs);
-
- const categories = ["All", ...getUniqueCategories()];
+ const [searchInput, setSearchInput] = React.useState("");
+ const [searchTerm, setSearchTerm] = React.useState("");
+ const [filteredBlogs, setFilteredBlogs] = React.useState(blogs);
- // Filter blogs based on search term and category
- useEffect(() => {
+ // Filter blogs after the user submits the blog search form.
+ React.useEffect(() => {
let filtered = blogs;
- // Filter by category
- if (selectedCategory !== "All") {
- filtered = filtered.filter((blog) => blog.category === selectedCategory);
- }
-
- // Filter by search term
if (searchTerm.trim() !== "") {
+ const normalizedSearch = searchTerm.trim().toLowerCase();
filtered = filtered.filter(
(blog) =>
- blog.title.toLowerCase().includes(searchTerm.toLowerCase()) ||
- blog.description.toLowerCase().includes(searchTerm.toLowerCase()) ||
+ blog.title.toLowerCase().includes(normalizedSearch) ||
+ blog.description.toLowerCase().includes(normalizedSearch) ||
blog.tags?.some((tag) =>
- tag.toLowerCase().includes(searchTerm.toLowerCase()),
+ tag.toLowerCase().includes(normalizedSearch),
),
);
}
setFilteredBlogs(filtered);
- }, [searchTerm, selectedCategory]);
+ }, [searchTerm]);
- const handleSearchChange = (e: React.ChangeEvent
+
@@ -581,4 +580,4 @@ const SelectComponent = ({
);
};
-export default OurProjects;
+export default ShowcasePage;
diff --git a/src/components/ui/FirebaseAuthGithub.tsx b/src/components/ui/FirebaseAuthGithub.tsx
deleted file mode 100644
index 436c2c0d..00000000
--- a/src/components/ui/FirebaseAuthGithub.tsx
+++ /dev/null
@@ -1,115 +0,0 @@
-import React, { useEffect, useState } from "react";
-import { auth } from "../../lib/firebase";
-import { GithubAuthProvider, signInWithPopup, User } from "firebase/auth";
-
-const uiConfig = {
- signInFlow: "popup",
- signInOptions: [
- {
- provider: GithubAuthProvider.PROVIDER_ID,
- // You can add scopes and custom parameters here if needed
- },
- ],
- callbacks: {
- signInSuccessWithAuthResult: () => false, // Prevents redirect
- },
-};
-
-const FirebaseAuthGithub: React.FC = () => {
- const [user, setUser] = useState(null);
- const [githubText, setGithubText] = useState("Sign in with GitHub"); // β
new state
-
- useEffect(() => {
- const unregisterAuthObserver = auth.onAuthStateChanged((user) =>
- setUser(user as User),
- );
-
- // β
new effect to change text on resize
- const handleResize = () => {
- if (window.innerWidth <= 1110) {
- setGithubText("Sign in");
- } else {
- setGithubText("Sign in with GitHub");
- }
- };
-
- handleResize(); // initial call
- window.addEventListener("resize", handleResize);
-
- return () => {
- unregisterAuthObserver();
- window.removeEventListener("resize", handleResize);
- };
- }, []);
-
- if (user) {
- return (
- (null);
+
+ useEffect(() => {
+ setContainer(document.getElementById("clerk-auth-navbar"));
+ }, []);
+
+ if (!customFields?.clerkPublishableKey || !container) {
+ return null;
+ }
+
+ return createPortal((null);
-
- useEffect(() => {
- setContainer(document.getElementById("firebase-auth-github-navbar"));
- }, []);
-
- return container ? createPortal(
+
- 
-
- -
- );
- }
-
- const handleGithubSignIn = async () => {
- const provider = new GithubAuthProvider();
- try {
- await signInWithPopup(auth, provider);
- } catch (error) {
- console.error("GitHub sign-in error:", error);
- }
- };
-
- return (
- - -
-
-
- );
-};
-
-export default FirebaseAuthGithub;
diff --git a/src/components/ui/NavbarClerkAuth.tsx b/src/components/ui/NavbarClerkAuth.tsx
new file mode 100644
index 00000000..f2878b29
--- /dev/null
+++ b/src/components/ui/NavbarClerkAuth.tsx
@@ -0,0 +1,38 @@
+import React, { useEffect, useState } from "react";
+import useDocusaurusContext from "@docusaurus/useDocusaurusContext";
+import { Show, SignInButton, UserButton } from "@clerk/react";
+import { createPortal } from "react-dom";
+
+const NavbarClerkAuthContent: React.FC = () => {
+ return (
+
+
+
+
+
+
+
+
+
+ );
+};
+
+const NavbarClerkAuth: React.FC = () => {
+ const {
+ siteConfig: { customFields },
+ } = useDocusaurusContext();
+ const [container, setContainer] = useState
{/* Hero section */}
@@ -1320,6 +1319,36 @@ const GithubBadgesContent = (): React.ReactElement => {
/>
{" "}
+
+
+ 
+
+
+
+
+ GitHub Agentic AI Developer
++ The GitHub Certified: Agentic AI Developer certification validates your expertise in creating intelligent automated workflows, building AI-powered agents, and extending GitHub's capabilities. +
+ + Prepare for the GitHub Agentic AI Developer exam + +
- {/* Sidebar */}
-