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Lakeflow pipelines are the most common way to build materialized views and streaming tables with Apache Spark™ Declarative Pipelines on Azure Databricks. You can also define standalone materialized views and streaming tables outside of a Lakeflow pipeline using simple query syntax, and Azure Databricks manages the underlying pipelines for you. Standalone tables can be created and refreshed from a Databricks SQL warehouse or from a notebook running on serverless general compute.
To decide between standalone tables and a Lakeflow pipeline, see Standalone pipelines vs. Lakeflow pipelines. To build materialized views and streaming tables as part of a managed pipeline, see Spark Declarative Pipelines on Lakeflow.
Note
This section was previously called "Pipelines for Databricks SQL." It was renamed to "Standalone materialized views and streaming tables" to reflect new support for creating standalone materialized views and streaming tables from a notebook running on serverless general compute, in addition to a Databricks SQL warehouse.
The following topics cover standalone materialized views and streaming tables.
| Topic | Description |
|---|---|
| Requirements for standalone pipelines | Learn about the compute options for standalone materialized views and streaming tables, including feature support and regional availability. |
| Use standalone streaming tables | Create, refresh, configure, and monitor streaming tables. |
| Use standalone materialized views | Create, refresh, and query materialized views. |
| When to use REPLACE WHERE vs. materialized views | Choose between REPLACE WHERE flows and materialized views for batch extract, transform, and load (ETL) workloads. |
To create and refresh standalone materialized views and streaming tables with Python from a notebook, see Use Python with standalone pipelines.