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-[Step 7a: Setting up Airflow](#step-7a-setting-up-airflow)
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-[Step 7b: Examine the Airflow DAG](#step-7b-examine-the-airflow-dag)
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-[Q: What if different feature views have different freshness requirements?](#q-what-if-different-feature-views-have-different-freshness-requirements)
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-[Step 7c: Enable the Airflow DAG](#step-7c-enable-the-airflow-dag)
@@ -220,11 +221,13 @@ You can speed up / scale this up in different ways:
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To run many parallel materialization jobs, you'll want to use the **SQL registry** (which is already used in this module).
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Then you could run multiple materialization jobs in parallel (e.g. using `feast materialize [FEATURE_VIEW_NAME] start_time end_time`)
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### Step 7a: Scheduling materialization
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### Step 7: Scheduling materialization
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To ensure fresh features, you'll want to schedule materialization jobs regularly. This can be as simple as having a cron job that calls `feast materialize-incremental`.
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Users may also be interested in integrating with Airflow, in which case you can build a custom Airflow image with the Feast SDK installed, and then use a `PythonOperator` (with `store.materialize`).
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#### Step 7a: Setting up Airflow
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We setup a standalone version of Airflow to set up the PythonOperator (Airflow now prefers @task for this).
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