Yes, in Databricks, it is possible to set up Liquid Clusteringโboth manual and automaticโon destination tables that store data ingested from Google Analytics 4 (GA4). This feature significantly improves table management and query performance compared to traditional partitioning or ZORDER techniques.
Manual Liquid Clustering Setup
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Liquid clustering can be enabled during table creation using the CLUSTER BY clause in the SQL statement or corresponding DataFrame/DeltaTable APIs in Python/Scala.
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Hereโs an example for manual clustering:
CREATE TABLE ga4_events (...) CLUSTER BY (some_column);
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This makes clustering flexible, allowing the clustering key to be redefined later without rewriting the historical data.
Automatic Liquid Clustering
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As of Databricks Runtime 15.4 LTS and above, automatic liquid clustering is generally available for Unity Catalogโmanaged tablesโa common setup when using Lakeflow or modern ingestion pipelines.
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You can enable automatic clustering by using CLUSTER BY AUTO when creating the table.
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Automatic liquid clustering intelligently selects the best key(s) to optimize performance, based on actual query usage and data patterns.
Key Considerations
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Liquid clustering is not compatible with legacy partitioning or ZORDER, so these should be avoided when ingesting new data.
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It is available and recommended for new streaming or batch tables, including those used as GA4 ingestion destinations.
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Make sure the Databricks workspace is running a supported Databricks Runtime version (15.2+ for manual, 15.4+ for automatic liquid clustering).
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For Unity Catalogโmanaged tables, which are typical for ingestion use cases, both manual and automatic liquid clustering are fully supported and recommended, ensuring optimal data management moving forward.
In summary, when ingesting data from GA4 into Databricks, Liquid Clusteringโmanual or automaticโcan be enabled on the destination table, providing robust data layout optimization and query acceleration.
