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This feature is currently in preview.
Materialized lake views in Microsoft Fabric facilitate the implementation of a medallion architecture to enhance data management. This functionality aids in the creation, management, and monitoring of views, and it improves transformations through a declarative approach. Developers can concentrate on generating insights derived from data rather than dealing with infrastructure maintenance.
Key features and benefits
Declarative pipelines: With materialized lake views, you can manage data transformations by using a declarative approach. This approach streamlines execution without the need to manually configure or maintain individual pipelines. This approach also supports defining data quality rules and specifying how to handle any violations that arise.
Visualization and monitoring: You can visualize lineage across all entities in a lakehouse, view the dependencies, and track execution progress. The processing pipeline is optimized for performance by updating the data in the appropriate sequence, managing optimal parallel paths, and refreshing only the parts of the lineage that changed.
This feature offers an integrated report that highlights data quality trends. You can also configure alerts based on any condition related to violations of a data quality rule.
Current limitations
The following features are currently not available for materialized lake views in Microsoft Fabric:
- Declarative syntax support for PySpark. You can use Spark SQL syntax to create and refresh materialized lake views.
- Incremental refresh capabilities to enhance data freshness and efficiency. All refresh operations are performed as full refreshes.
- API support for managing materialized lake views.
- Cross-lakehouse lineage and execution features.