Apache Iceberg is an open-source, community-driven table format specifically designed for large analytic datasets. It is a high-performance format that simplifies data processing tasks on large datasets stored in data lakes, and is known for being fast, efficient, and reliable at any scale. Apache Iceberg enables the use of SQL tables for big data, facilitating various engines like Spark, Trino, Flink, Presto, Hive, and Impala to work with the same tables simultaneously, thereby improving data reliability and performance across different data processing engines.
The core idea behind Apache Iceberg is to resolve challenges associated with traditional catalogues and bring the reliability and simplicity of SQL tables to big data analytics. It provides a more structured, consistent, and efficient way of handling massive datasets, while ensuring a high level of performance. Apache Iceberg manages data in data lakes efficiently, keeps records of how datasets change over time, and avoids common pitfalls associated with schema evolution. By doing so, it is rapidly becoming an industry standard for managing data in data lakes. It delivers a significant advantage in data engineering and analytics domains by ensuring that data remains highly accessible and manageable, even as it scales across large distributed systems.