Databricks tables
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 |
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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 |
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Tables where Databricks manages both metadata and data files. Recommended for new tables with optimized performance and storage. | |
Tables that reference data stored in external storage systems while managing metadata in Unity Catalog. | |
Read-only tables representing data in external systems connected through Lakehouse Federation. |
Storage formats
Work with open table formats that provide advanced data management capabilities.
Format | Description |
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Default storage format providing ACID transactions, time travel, and schema enforcement for managed and external tables. | |
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 |
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Define and enforce data quality rules with check constraints and not null constraints. | |
Control how Databricks handles schema changes and data type enforcement during writes. | |
Organize data by partition keys to improve query performance and data management. | |
Monitor and analyze table storage usage and growth patterns. | |
Migrate external tables to managed tables for improved performance and management. | |
Automatically discover and register partitions in external tables stored in cloud storage. |