Use pipelines in Databricks SQL

Lakeflow Spark Declarative Pipelines is the most common way to work with data in pipelines. You can define streaming tables and materialized views with simple query syntax, and Azure Databricks manages the pipelines for you. Pipeline functionality is also available for use outside of Lakeflow Spark Declarative Pipelines using Databricks SQL.

This section teaches you about using pipelines outside of Lakeflow Spark Declarative Pipelines, including the following topics.

Topic	Description
Use streaming tables in Databricks SQL	Learn how to create, refresh, configure, and monitor Databricks streaming tables in Databricks SQL.
Use materialized views in Databricks SQL	Learn how to create, refresh, and query Databricks materialized views in Databricks SQL.

Additional resources

• Databricks SQL language reference
• Lakeflow Spark Declarative Pipelines SQL language reference
• Develop Lakeflow Spark Declarative Pipelines
• Use ALTER statements with pipeline datasets