Delta Lake and Apache Iceberg are two prominent open-source table formats designed to address the challenges of managing large-scale datasets in data lakes. While they share a common goal, their histories and development paths have diverged in significant ways.
- Delta Lake: Uses a merge-on-write approach, where updates and deletes are first written to a transaction log and then merged into the existing data files. This ensures strong consistency but can potentially impact write performance.
- Iceberg: Employs a merge-on-read strategy, where updates and deletes are tracked in a separate metadata file. Queries are performed by merging the base data with the changes during read time. This can provide better write performance but might introduce a slight overhead during query execution.
- Delta Lake: Offers a more rigid approach to schema evolution, requiring explicit schema changes and potential data rewrites.
- Iceberg: Provides a more flexible schema evolution mechanism, allowing for adding or removing columns without affecting existing data.
- Delta Lake: Generally offers better write performance due to its merge-on-write approach.
- Iceberg: May have slightly lower write performance but can often achieve comparable or better read performance, especially for large datasets.
- Delta Lake: Is tightly integrated with Databricks and its ecosystem, providing seamless integration with other Databricks tools.
- Iceberg: Is more vendor-neutral and can be used with various data processing frameworks and platforms.
- Delta Lake: If you prioritize strong consistency, tight integration with Databricks, and a more rigid schema evolution approach.
- Iceberg: If you require a more flexible schema evolution, better read performance for large datasets, and a vendor-neutral approach.
Ultimately, the best choice between Apache Iceberg and Delta Lake depends on your specific use case, data requirements, and the ecosystem you are working with.
Damien Gaioni, BI Consultant
