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.

Keys differences

1 - Transaction Model:

• 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.

2 - Schema Evolution:

• 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.

3 - Performance:

• 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.

4 - Ecosystem:

• 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.

When to Choose Which:

• 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.