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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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‎README.md‎

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feast materialize --disable-event-timestamp
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```
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The `--disable-event-timestamp` flag allows you to materialize features using the current datetime without needing to specify start and end timestamps. This is useful for quick testing or when you want to materialize all available data up to now.
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The `--disable-event-timestamp` flag allows you to materialize all available feature data using the current datetime as the event timestamp, without needing to specify start and end timestamps. This is useful when your source data lacks proper event timestamp columns.
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```commandline
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Materializing feature view driver_hourly_stats from 2021-04-14 to 2021-04-15 done!

‎docs/getting-started/components/online-store.md‎

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# Online store
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Feast uses online stores to serve features at low latency.
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Feature values are loaded from data sources into the online store through _materialization_, which can be triggered through the `materialize` command.
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The storage schema of features within the online store mirrors that of the original data source.
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One key difference is that for each [entity key](../concepts/entity.md), only the latest feature values are stored.
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No historical values are stored.
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Here is an example batch data source:
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![](../../.gitbook/assets/image%20%286%29.png)
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Once the above data source is materialized into Feast (using `feast materialize`), the feature values will be stored as follows:
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![](../../.gitbook/assets/image%20%285%29.png)
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# Online store
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Feast uses online stores to serve features at low latency.
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Feature values are loaded from data sources into the online store through _materialization_, which can be triggered through the `materialize` command (either with specific timestamps or using `--disable-event-timestamp` to materialize all data with current timestamps).
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The storage schema of features within the online store mirrors that of the original data source.
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One key difference is that for each [entity key](../concepts/entity.md), only the latest feature values are stored.
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No historical values are stored.
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Here is an example batch data source:
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![](../../.gitbook/assets/image%20%286%29.png)
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Once the above data source is materialized into Feast (using `feast materialize` with timestamps or `feast materialize --disable-event-timestamp`), the feature values will be stored as follows:
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![](../../.gitbook/assets/image%20%285%29.png)
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Features can also be written directly to the online store via [push sources](../../reference/data-sources/push.md) .

‎docs/getting-started/components/overview.md‎

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* **Create Batch Features:** ELT/ETL systems like Spark and SQL are used to transform data in the batch store.
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* **Create Stream Features:** Stream features are created from streaming services such as Kafka or Kinesis, and can be pushed directly into Feast via the [Push API](../../reference/data-sources/push.md).
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* **Feast Apply:** The user (or CI) publishes versioned controlled feature definitions using `feast apply`. This CLI command updates infrastructure and persists definitions in the object store registry.
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* **Feast Materialize:** The user (or scheduler) executes `feast materialize` which loads features from the offline store into the online store.
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* **Feast Materialize:** The user (or scheduler) executes `feast materialize` (with timestamps or `--disable-event-timestamp` to materialize all data with current timestamps) which loads features from the offline store into the online store.
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* **Model Training:** A model training pipeline is launched. It uses the Feast Python SDK to retrieve a training dataset that can be used for training models.
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* **Get Historical Features:** Feast exports a point-in-time correct training dataset based on the list of features and entity dataframe provided by the model training pipeline.
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* **Deploy Model:** The trained model binary (and list of features) are deployed into a model serving system. This step is not executed by Feast.

‎docs/getting-started/concepts/data-ingestion.md‎

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feast materialize-incremental $CURRENT_TIME
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```
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**Simple materialization (uses current datetime):**
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**Simple materialization (for data without event timestamps):**
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```bash
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feast materialize --disable-event-timestamp
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```

‎docs/getting-started/quickstart.md‎

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{% endtab %}
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{% tab title="Bash (simple)" %}
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```bash
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# Alternative: Use current datetime without specifying timestamps
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# Alternative: Materialize all data using current timestamp (for data without event timestamps)
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feast materialize --disable-event-timestamp
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```
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{% endtab %}

‎docs/reference/feast-cli-commands.md‎

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feast materialize --disable-event-timestamp -v driver_hourly_stats
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```
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The `--disable-event-timestamp` flag is useful for quick testing or when you want to materialize all available data up to the current time.
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The `--disable-event-timestamp` flag is useful when your source data lacks event timestamp columns, allowing you to materialize all available data using the current datetime as the event timestamp.
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```text
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Materializing 1 feature views from 2020-01-01 to 2022-01-01

‎infra/templates/README.md.jinja2‎

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feast materialize --disable-event-timestamp
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```
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The `--disable-event-timestamp` flag allows you to materialize features using the current datetime without needing to specify start and end timestamps. This is useful for quick testing or when you want to materialize all available data up to now.
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The `--disable-event-timestamp` flag allows you to materialize all available feature data using the current datetime as the event timestamp, without needing to specify start and end timestamps. This is useful when your source data lacks proper event timestamp columns.
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```commandline
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Materializing feature view driver_hourly_stats from 2021-04-14 to 2021-04-15 done!

‎sdk/python/feast/cli/cli.py‎

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# limitations under the License.
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import json
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import logging
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from datetime import datetime
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from datetime import datetime, timedelta
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from importlib.metadata import version as importlib_version
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from pathlib import Path
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from typing import List, Optional
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@click.option(
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"--disable-event-timestamp",
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is_flag=True,
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help="Use current datetime for materialization instead of requiring timestamps",
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help="Materialize all available data using current datetime as event timestamp (useful when source data lacks event timestamps)",
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)
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@click.pass_context
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def materialize_command(
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START_TS and END_TS should be in ISO 8601 format, e.g. '2021-07-16T19:20:01'
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If --disable-event-timestamp is used, timestamps are not required and datetime.now() will be used.
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If --disable-event-timestamp is used, timestamps are not required and all available data will be materialized using the current datetime as the event timestamp.
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"""
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store = create_feature_store(ctx)
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"Cannot specify START_TS or END_TS when --disable-event-timestamp is used"
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)
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now = datetime.now()
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start_date = now
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# Query all available data and use current datetime as event timestamp
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start_date = datetime(1970, 1, 1) # Beginning of time to capture all historical data
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end_date = now
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else:
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if not start_ts or not end_ts:

‎sdk/python/feast/feature_store.py‎

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end_date (datetime): End date for time range of data to materialize into the online store
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feature_views (List[str]): Optional list of feature view names. If selected, will only run
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materialization for the specified feature views.
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disable_event_timestamp (bool): If True, uses current datetime for materialization instead of event timestamps
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disable_event_timestamp (bool): If True, materializes all available data using current datetime as event timestamp instead of source event timestamps
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Examples:
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Materialize all features into the online store over the interval

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