Delta tables and Iceberg are both technologies that enable big data management on distributed systems such as Apache Spark, but there are several key differences between the two.
1. Data format: Delta tables use the Parquet format for storage, while Iceberg uses the Avro format. This means that data stored in Delta tables can be read by other systems that support Parquet, while data stored in Iceberg tables is limited to systems that support Avro.
2. Data management: Delta tables offer a more robust set of data management features, such as support for ACID transactions, time travel, and schema evolution. Iceberg, on the other hand, focuses more on data organization and data lineage.
3. Performance: Delta tables are optimized for performance and are able to handle high write and read loads, while Iceberg is optimized for data organization and lineage.
4. Compatibility: Delta tables are natively supported by Databricks, and thus work seamlessly with the Databricks platform. Iceberg is not natively supported by Databricks and need additional configuration to work with it.
5. Data Governance: Delta tables include built-in data governance features such as data lineage, audit logging and data masking. Iceberg, however, does not have built-in data governance features and rely on external tools for data governance.
Delta tables and Iceberg are both powerful technologies for managing big data on distributed systems, but they are designed for different use cases. Delta tables are more geared towards high-performance data processing and analytics, while Iceberg is better suited for data organization and lineage management.