BerriAI / litellm
github.com·3d ago
Developer Tools
Databricks Blog·Aug 25·5 min read
Modernizing SQL ETL in Lakehouse with Declarative Patterns
TL;DR
Databricks has introduced declarative ETL patterns for SQL users in Lakehouse, simplifying complex data transformations directly within SQL queries. This enhancement allows for easier management of recurring ETL tasks without extensive coding.
✦ Why It Matters
Start using declarative ETL patterns in your SQL workflows today to simplify data management tasks.
Key Takeaways
- Utilize declarative patterns to simplify SQL ETL tasks.
- Implement AUTO CDC for efficient change data capture.
- Adopt REPLACE WHERE for targeted batch updates.
- Leverage Genie Code to generate declarative flows easily.
How It Works
Declarative ETL allows users to specify the desired state of tables or views, while Databricks manages the underlying logic for updates, scheduling, and incremental processing. For example, users can define an APPEND flow that automatically tracks new records, eliminating the need for manual insert logic.
Similarly, AUTO CDC simplifies the handling of CDC data by allowing users to specify keys and sequencing with minimal code.