implement-incremental-extraction

Incremental metadata extraction in SQL connectors processes only recent schema changes using customized SQL templates and state management. This optimizes update performance and reduces resource consumption during metadata refreshes.

Incremental extraction updates metadata by detecting only recent table and column changes instead of full refreshes. It is needed when managing large database schemas where full metadata updates consume excessive runtime and resources.

State management with RocksDB and DuckDB handles persistence for incremental extraction workflows. It detects backfill scenarios, manages change histories, and tracks processed metadata to maintain extraction continuity across runs.

Yes, customizing SQL templates for incremental extraction requires database-specific SQL syntax knowledge. You must create tailored SQL scripts to accurately detect table and column changes for your target database system.

Yes, the Application SDK supports integrating incremental extraction workflows with existing metadata pipelines. It provides the architecture for state storage mechanisms and SQL template customization to optimize connector performance.

Incremental metadata extraction requires compatible state management strategies and database-specific SQL syntax. Limitations include handling complex backfill scenarios and ensuring state persistence reliability across different storage engines like RocksDB and DuckDB.