Azure Databricks tables

Azure Databricks supports multiple table types and storage formats to meet different data management needs. For an overview of table types, storage formats, and Unity Catalog integration, see Azure Databricks tables concepts.

Table types

Explore different table types and their capabilities for various data management scenarios.

Table type	Description
Unity Catalog managed tables in Azure Databricks for Delta Lake and Apache Iceberg	Azure Databricks manages metadata and data files for new tables that require optimized performance.
Temporary tables	Session-scoped Unity Catalog managed tables for intermediate data. SQL warehouses only.
Work with external tables	Data stored in external systems. Unity Catalog manages metadata only.
Work with foreign tables	Read-only access to data in external systems connected through Lakehouse Federation.

Storage formats

Work with open table formats that support advanced data management capabilities.

Format	Description
Delta Lake	Default storage format with ACID transactions, time travel, and schema enforcement for managed and external tables.
Apache Iceberg	Open table format for integration with the Iceberg ecosystem, supporting advanced metadata management.

Table management

Configure and optimize table behavior, structure, and performance.

Feature	Description
Table constraints	Define and enforce data quality rules with check constraints and not null constraints.
Schema enforcement	Control how Azure Databricks handles schema changes and data type enforcement during writes.
Table partitioning	Organize data by partition keys to improve query performance and data management.
Table size monitoring	Monitor and analyze table storage usage and growth patterns.
Convert external to managed	Migrate external tables to managed tables for improved performance and management.
External partition discovery	Automatically discover and register partitions in external tables stored in cloud storage.