Apache Iceberg
Apache Iceberg is a modern data management framework built to support large-scale data lakes with ACID transactions and flexible partitioning, making it highly scalable and efficient for managing complex datasets.
Key Features:
- ACID Transactions: Iceberg ensures atomic, consistent, isolated, and durable (ACID) operations on data lakes, enabling reliable updates, deletes, and upserts.
- Schema Evolution: Supports schema changes (adding/removing columns) without breaking existing queries, allowing the data model to evolve smoothly.
- Partition Evolution: Iceberg supports dynamic partitioning, allowing partitions to evolve over time without rewriting the entire dataset.
- Hidden Partitioning: Automatically manages partitions internally, improving query efficiency and eliminating the need for manual partition management.
- Versioning and Time Travel: Enables users to query previous versions of the dataset, making it possible to access historical data through "time travel" queries.
- Scalability: Designed to handle petabyte-scale datasets in cloud object storage, Iceberg optimizes queries and manages metadata efficiently for large-scale data lakes.
Use Cases:
- Large-scale analytics with frequent updates and evolving schema or partitioning needs.
- Time-travel queries to access historical versions of data.
- Efficient querying in large-scale data lakes with complex data models and flexible partitioning.