Databricks Delta Lake addresses challenges around data quality and consistency in complex data structures like iceberg tables in several ways:
1. Versioning - Delta Lake maintains a transaction log that tracks all the changes made to the data lake. This allows you to go back in time and view or query a previous version of the data. This is useful for auditing data quality issues or recovering from errors.
2. Schema enforcement - You can define a schema for your Delta table and Delta Lake will enforce that schema on all data written to that table. This prevents bad or inconsistent data from being written to the table.
3. Merges - The Delta Lake MERGE command allows you to merge new data into an existing Delta table while enforcing the schema and handling update/delete operations. This helps keep the table consistently up-to-date with the latest data.
4. Compactions - Periodic compactions on a Delta table consolidate small files into larger files and reclaim space from deleted records. This helps optimize the table for performance and cost efficiency. Compactions also validate the data integrity in the table.
5. Time travel - You can query previous versions of a Delta table by providing a timestamp. This allows you to go back and identify when data quality issues may have been introduced.
6. Audit history - Delta Lake maintains an audit history of all the operations performed on a table. This audit history can be useful for tracking down the source of data quality problems.
So in summary, Delta Lake provides capabilities like schema enforcement, versioning, merging changes, compaction, time travel, and auditing that help ensure high data quality and consistency, even for complex tables like iceberg tables.
