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Azure Databricks provides multiple table types and storage formats to meet different data management needs. This section covers managed, external, and foreign tables, along with the Delta Lake and Apache Iceberg storage formats that power advanced features like Atomicity, Consistency, Isolation, and Durability (ACID) transactions and time travel.
Core concepts
Learn the fundamentals of table types, storage formats, and Unity Catalog integration.
| Topic | Description |
|---|---|
| Tables concepts | Core concepts and foundational information about table types, storage formats, and Unity Catalog integration. |
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. Used for new tables requiring 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 provide advanced data management capabilities.
| Format | Description |
|---|---|
| Delta Lake | Default storage format providing 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. |