Time Travel in Delta Lake
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In Big Data Processing, we continuously process large amounts of data and store the resulting data in a data lake. This keeps changing the state of the data lake. But, sometimes, we would like to access a historical data version. This requires versioning of data. Such kinds of data management simplify our data pipeline by making it easy for professionals or organizations to audit the data changes, roll back to the previous version in case of accidental bad writes or deletes, etc. Apache Spark alone can’t provide these kinds of capabilities, but with the help of Databricks Delta, the next-gen unified analytics engine built on top of Apache Spark introduces such unique Time Travel capabilities.
When we write our data into a Delta table, every operation is automatically versioned, and we can access any data version. This allows us to travel back to a different version of the current delta table.
This time-traveling can be achieved using Two approaches:
- Using a version number
- Using a timestamp
Time Travel Use Cases
Delta Lake time travel allows us to query an older snapshot of a Delta Lake table. Time travel has many use cases, including,
- Time travel makes it easy to do rollbacks in case of bad writes, playing an essential role in fixing mistakes in our data.
- It helps in re-creating analysis, reports, or outputs.
- It also simplifies time-series analytics.