databricks-dbsql

Stored procedures in Databricks SQL allow writing procedural logic, conditional statements, and error handling directly in SQL. You can define input parameters, implement loops, and execute conditional data processing within the Lakehouse Platform.

Materialized views in Databricks SQL optimize query performance by pre-computing and storing complex query results. They automatically refresh underlying data, enabling faster read access for heavy analytical workloads and complex data modeling tasks.

Databricks SQL supports advanced geospatial operations using H3 indexing and ST functions. You can perform spatial analysis, manipulate geometric data, and run complex location-based queries directly within your SQL data pipelines.

AI functions in Databricks SQL integrate AI models directly into standard queries, allowing you to embed machine learning predictions and generative AI capabilities within traditional SQL data processing workflows.

Databricks SQL supports recursive Common Table Expressions for hierarchical data querying and database transactions for reliable data manipulation. These features facilitate complex data transformations and ensure data integrity during processing.

Pipe syntax in Databricks SQL provides an alternative query structure for chaining data transformations sequentially. It improves query readability for complex data pipelines by passing results from one operation directly to the next.