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Merged
sanjay-kv merged 5 commits into from
May 12, 2026
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image fix for medallion architecture #1464

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258a85c
Update index.md
Adez017 May 8, 2026
42e2cca
Merge branch 'recodehive:main' into dicord
Adez017 May 11, 2026
77cc03b
Fix broken image paths in medallion architecture blog
Copilot May 11, 2026
50f6538
Merge pull request #28 from Adez017/copilot/fix-errors-in-recode-website
Adez017 May 11, 2026
e80fd20
added ref to code section to avoid plag
sanjay-kv May 12, 2026
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6 changes: 4 additions & 2 deletions blog/medallion-architecture/index.md
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Expand Up @@ -61,7 +61,7 @@ The architecture divides your data journey into three layers:

Each layer has one job. Each layer makes the data a little more trustworthy. By the time data reaches the end, it's reliable, consistent, and ready to power real business decisions.

<!-- ![Three-layer Medallion Architecture flow diagram](./img/medallion-architecture-flow.png) -->
![Three-layer Medallion Architecture flow diagram](./Img/medallion-architecture-flow.png)


## 🥉 Bronze: The "Keep Everything" Layer
Expand Down Expand Up @@ -121,6 +121,7 @@ Most of the heavy lifting in a data pipeline lives here. It's not glamorous work
### What Silver looks like in practice

Here's a simple PySpark transformation from Bronze to Silver:
- [Reference code](https://oneuptime.com/blog/post/2026-02-17-how-to-build-a-data-lakehouse-architecture-on-gcp-using-cloud-storage-dataproc-and-bigquery/view)

```python
from pyspark.sql import SparkSession
Expand Down Expand Up @@ -276,7 +277,7 @@ In practice, teams often extend the model:
- **Feature layer** — prepared datasets for ML model training, maintained by data science teams on top of Silver
- **Semantic layer** — business-friendly models sitting between Gold and end users for self-serve BI

<!--![Extended Medallion Architecture with optional Landing, Feature, and Semantic layers](./img/medallion-extended-layers.png) -->
![Extended Medallion Architecture with optional Landing, Feature, and Semantic layers](./Img/medallion-extended-layers.png)

The three-tier model is a starting point, not a ceiling. The right number of layers is whatever your team actually needs.

Expand Down Expand Up @@ -330,6 +331,7 @@ This is the exact structure we adopted after the revenue incident. Bronze preser
- [RecodeHive - Lakehouse vs Data Warehouse](https://www.recodehive.com/blog/lakehouse-vs-warehouse)
- [RecodeHive - Microsoft Fabric: One Platform, One Lake](https://www.recodehive.com/blog/microsoft-fabric-explained)
- [RecodeHive - Azure Storage & ADLS Gen2](https://www.recodehive.com/blog/azure-storage-options)
- [OneUptime - Build a Data Lakehouse on GCP](https://oneuptime.com/blog/post/2026-02-17-how-to-build-a-data-lakehouse-architecture-on-gcp-using-cloud-storage-dataproc-and-bigquery/view)

## About the Author

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