Transaction log as the heart of Delta Lake
summary
Delta Lake stores every change as numbered JSON commits (names use up to 20 zero‑padded digits) in a transaction log alongside Parquet files so Spark replays events to reconstruct any version. Hands‑on PySpark shows create/convert/append, delete/update, merge upsert, schema enforcement versus merge‑schema evolution, checkpoints as Parquet with a last_checkpoint pointer, time travel by version or timestamp, optimize/compaction to reduce small files and vacuum to delete old Parquet, plus protocol/table features like column mapping and IDENTITY columns.
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chapters (10)
- 0:00Delta Lake intro
- 10:19What is a Delta table
- 27:31Creating Delta tables
- 33:15Appending/deleting data
- 38:59Updating/merging data
- 48:15Time travel in Delta
- 56:21Schema enforcement/evolution
- 1:03:05Optimizing Delta tables
- 1:11:40Protocol versions
- 1:16:37Delta Lake resources
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