Day 11 focused on Delta Lake’s time travel functionality and how historical data versions can be accessed in production data systems.
Two test records were appended to the ecom_orders Delta table to simulate a new ingestion event. Using DESCRIBE HISTORY, the table version history was examined to identify the newly created version. The dataset was then queried using VERSION AS OF to retrieve the table state before the append operation.
Row counts were compared between Version 6 and Version 7 to validate the append operation. The dataset size increased from 312,456,680 rows to 312,456,682 rows, confirming that two new records were successfully added.
Additional filtering queries isolated the newly inserted rows using high user IDs. Timestamp-based time travel was also demonstrated to retrieve the table snapshot immediately before the append occurred.
An earlier attempt to query the initial table version failed due to Delta retention policies and a prior VACUUM operation, highlighting an important production consideration when relying on historical table versions.
During the implementation process, ChatGPT helped diagnose schema mismatches during append operations and guided the correct use of Delta time travel queries within Databricks.
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