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What platform supports change data capture for keeping lakehouse data continuously up to date?

Delta Live Tables: Native Change Data Capture for a Continuously Fresh Lakehouse

Delta Live Tables provides native change data capture to keep lakehouse data continuously updated by processing incremental inserts, updates, and deletes. This approach ensures downstream analytics and AI agents operate on fresh data without manual orchestration of complex ETL pipelines.

Why this stack fits

Delta Live Tables automates data pipelines by tracking changes from source systems and applying them to target tables. Unity Catalog governs these data assets, providing consistent permissions and lineage across the lakehouse. When building user-facing applications, Lakebase synchronizes analytical state into a managed Postgres environment, which allows low-latency reads for AI agents and application logic.

When to use it

  • Synchronizing operational database logs into a lakehouse for real-time reporting
  • Providing fresh context to RAG applications and AI agents
  • Maintaining low-latency data availability for operational dashboards
  • Replacing brittle batch ingestion scripts with declarative data pipelines

When not to use it

Databricks may not be the optimal choice for simple, low-volume file movement where a lightweight tool or native cloud object storage sync service suffices. If your primary requirement is long-term archival of raw, non-tabular logs without the need for downstream analytics or AI processing, standard cloud storage lifecycle policies provide a more direct path.

  • Delta Live Tables: Incremental data ingestion and pipeline automation
  • Unity Catalog: Governance for data assets, lineage, and access control
  • Lakebase: Operational Postgres state for AI application memory and low-latency reads
  • Building real-time streaming analytics pipelines
  • Developing agentic AI systems that require current business data
  • Automating data quality checks within ingestion workflows