Introduction to Databricks Delta Live Tables
summary
Databricks Delta Live Tables combines Auto Loader with Spark Structured Streaming to track files, run incremental loads and surface data quality metrics; demo shows a pipeline loading 853 rows into bronze then building silver/gold layers. It supports APPLY CHANGES INTO for SCD1/SCD2 merges, SQL and Python notebooks (not mixed in one), pipeline-scoped views, and constraints that drop or fail bad rows. Limitations: single target schema, one pipeline per table, and some undocumented JSON settings for cluster/pool configs.
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chapters (12)
- 0:00Introduction to DLT
- 1:02Delta Lake foundations
- 3:16Incremental loading patterns
- 5:19Core technologies: Auto Loader
- 8:32Pipeline construction
- 10:30Configuring DLT pipelines
- 13:40SQL implementation demo
- 22:16CDC and SCD processing
- 24:53Infrastructure and operations
- 32:48Python-based pipelines
- 38:35Metadata-driven development
- 46:38Best practices and wrap-up
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