databricks-dbsql

To create a stored procedure in Databricks SQL, you use SQL scripting to encapsulate multi-statement logic, enabling you to perform upserts with built-in error handling and logging for efficient reuse.

Materialized views in Databricks SQL precompute and cache complex query results, significantly accelerating performance tuning by allowing repeated access to aggregated data without reprocessing the underlying tables.

Yes, Databricks SQL supports advanced geospatial functions including H3 indexing and ST functions, allowing you to perform spatial analysis and manipulate geometric data directly within your Lakehouse queries.

Databricks SQL fully supports recursive CTEs for hierarchical data traversal and provides transaction capabilities, ensuring ACID compliance and reliable multi-statement data processing workflows.

The best way to structure data models on the Databricks Lakehouse involves following SQL data modeling best practices, utilizing temporary tables, pipe syntax, and Unity Catalog integration for optimized organization.

You should use SQL scripting in Databricks SQL when you need to execute complex, multi-statement procedural logic, manage control flow, or build reusable routines like stored procedures that standard queries cannot handle.