Building on open table formats

Open table formats like Apache Iceberg, Delta Lake, and Apache Hudi provide a layer of abstraction over raw data files, enabling features such as ACID transactions, schema evolution, and time travel. These specifications define how to store and manage large datasets in a structured manner on distributed storage systems, providing consistency and reliability across multiple processing engines. The primary open table formats in use today are designed to solve distinct challenges, with Iceberg shining in analytics scenarios, Delta Lake best suited for Spark environments, and Hudi optimized for streaming-centric environments. Iceberg addresses the need for database-like capabilities in object storage systems, enabling reliable access to data that would otherwise be locked in raw files. Its key features include ACID transactions, schema evolution, time travel, and interoperability, making it an essential component for modern data platforms that need to scale. The combination of Iceberg and Amazon S3 offers a potent alternative to traditional proprietary data lakes and data warehouses, enabling companies to offload their data to lower-cost object storage systems while still being able to query and use the data as if it were in a more traditional data environment.

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