add docs changes

docs/docs-beta/docs/guides/quality-testing/data-freshness-testing.md

@@ -1,4 +1,43 @@
 ---
 title: "Test for data freshness"
 sidebar_position: 20
----
+---
+Freshness checks provide a way to identify data assets that are overdue for an update.
+
+This guide covers how to construct freshness checks for materializable [assets](/todo) and [external assets](/todo).
+
+<details>
+  <summary>Prerequisites</summary>
+
+To follow the steps in this guide, you'll need:
+
+- Familiarity with [assets](/todo)
+- Familiarity with [asset checks](/todo)
+
+</details>
+
+## Test data freshness for materializable assets
+
+The example below defines a freshness check on an asset that fails if the asset's latest materialization occurred more than one hour before the current time.
+
+Defining a schedule or sensor is required to ensure the freshness check executes. If the check only runs after the asset has been materialized, the check won't be able to detect the times materialization fails.
+
+<CodeExample filePath="guides/data-assets/quality-testing/freshness-checks/materializable-asset-freshness-check.py" language="python" title="Test data freshness for materializable assets" />
+
+## Test data freshness for external assets
+
+To run freshness checks on external assets, the checks need to know when the external assets were last updated. Emitting these update timestamps in observation metadata allows Dagster to calculate whether the asset is overdue.
+
+The example below defines a freshness check and adds a schedule to run the check periodically.
+
+<CodeExample filePath="guides/data-assets/quality-testing/freshness-checks/external-asset-freshness-check.py" language="python" title="Test data freshness for external assets" />
+
+### Use anomaly detection to test data freshness (Dagster+ Pro)
+
+Instead of applying policies on an asset-by-asset basis, Dagster+ Pro users can take advantage of a time series anomaly detection model to determine if data is arriving later than expected.
+
+<CodeExample filePath="guides/data-assets/quality-testing/freshness-checks/anomaly-detection.py" language="python" title="Use anomaly detection to detect overdue assets" />
+
+## Next steps
+
+- Explore more [asset checks](/todo)

examples/docs_beta_snippets/docs_beta_snippets/guides/data-assets/quality-testing/freshness-checks/anomaly-detection.py

@@ -0,0 +1,7 @@
+from dagster_cloud.anomaly_detection import build_anomaly_detection_freshness_checks
+
+hourly_sales = ...
+
+freshness_checks = build_anomaly_detection_freshness_checks(
+    assets=[hourly_sales], params=None
+)

examples/docs_beta_snippets/docs_beta_snippets/guides/data-assets/quality-testing/freshness-checks/external-asset-freshness-check.py

@@ -0,0 +1,55 @@
+from datetime import timedelta
+
+import dagster_snowflake as dg_snowflake
+
+import dagster as dg
+
+
+@dg.observable_source_asset(specs=[dg.AssetSpec("hourly_sales")])
+def hourly_sales(snowflake: dg_snowflake.SnowflakeResource):
+    with snowflake.get_connection() as conn:
+        freshness_results = dg_snowflake.fetch_last_updated_timestamps(
+            snowflake_connection=conn.cursor(),
+            tables=["hourly_sales"],
+            schema="PUBLIC",
+        )
+        for table_name, last_updated in freshness_results.items():
+            yield dg.ObserveResult(
+                asset_key=table_name,
+                metadata={
+                    "dagster/last_updated_timestamp": dg.MetadataValue.timestamp(
+                        last_updated
+                    )
+                },
+            )
+
+
+freshness_check_schedule = dg.ScheduleDefinition(
+    job=dg.define_asset_job(
+        "hourly_sales_observation_job",
+        selection=dg.AssetSelection.assets(hourly_sales),
+    ),
+    # Runs every minute. Usually, a much less frequent cadence is necessary,
+    # but a short cadence makes it easier to play around with this example.
+    cron_schedule="* * * * *",
+)
+
+
+hourly_sales_freshness_check = dg.build_last_update_freshness_checks(
+    assets=[hourly_sales],
+    lower_bound_delta=timedelta(hours=1),
+)
+
+
+defs = dg.Definitions(
+    assets=[hourly_sales],
+    asset_checks=hourly_sales_freshness_check,
+    schedules=[freshness_check_schedule],
+    resources={
+        "snowflake": dg_snowflake.SnowflakeResource(
+            user=dg.EnvVar("SNOWFLAKE_USER"),
+            account=dg.EnvVar("SNOWFLAKE_ACCOUNT"),
+            password=dg.EnvVar("SNOWFLAKE_PASSWORD"),
+        )
+    },
+)

examples/docs_beta_snippets/docs_beta_snippets/guides/data-assets/quality-testing/freshness-checks/materializable-asset-freshness-check.py

@@ -0,0 +1,22 @@
+from datetime import timedelta
+
+import dagster as dg
+
+
+@dg.asset
+def hourly_sales(context: dg.AssetExecutionContext):
+    context.log.info("Fetching and emitting hourly sales data")
+    ...
+
+
+hourly_sales_freshness_check = dg.build_last_update_freshness_checks(
+    assets=[hourly_sales], lower_bound_delta=timedelta(hours=1)
+)
+freshness_checks_sensor = dg.build_sensor_for_freshness_checks(
+    freshness_checks=hourly_sales_freshness_check
+)
+defs = dg.Definitions(
+    assets=[hourly_sales],
+    asset_checks=hourly_sales_freshness_check,
+    sensors=[freshness_checks_sensor],
+)

examples/experimental/dagster-airlift/examples/dbt-example/dbt_example/airflow_dags/dags.py

@@ -8,6 +8,7 @@
 from airflow.operators.bash import BashOperator
 from dagster_airlift.in_airflow import mark_as_dagster_migrating
 from dagster_airlift.migration_state import load_migration_state_from_yaml
+
 from dbt_example.shared.lakehouse_utils import load_csv_to_duckdb
 from dbt_example.shared.load_iris import CSV_PATH, DB_PATH, IRIS_COLUMNS