Apache Iceberg
Apache Iceberg has carved a niche as a high-performance open-source table format, enabling ACID transactions on petabyte-scale SQL tables. It aims to provide a compelling alternative for data lake management solutions.
Iceberg surpasses traditional formats like Parquet or ORC by offering distinct advantages:
- Schema Evolution: Allows modifications to table schemas without rewriting the entire table.
- Snapshot Isolation: Ensures data consistency by preventing readers and writers from interfering with each other.
- Efficient Metadata Management: Utilizes metadata to manage large-scale tables efficiently, minimizing overhead associated with vast datasets.
- Partition Pruning: Automatically prunes irrelevant partitions during queries, optimizing performance.
Note
PyIceberg is a Python implementation for accessing Iceberg tables, without the need of a JVM.
Getting Started
Basic Architecture
- Table Format: Employs a manifest list and manifest files to track metadata, facilitating efficient handling of large datasets.
- Snapshot Management: Each table maintains a history of snapshots, enabling time travel and rollback capabilities.
- Partitioning: Leverages hidden partitioning to simplify partition management for users, enhancing performance.
Use Cases
- Financial Services: Ideal for handling large-scale transactional data with ACID guarantees, making it suitable for financial applications.
- E-commerce Analytics: Can effectively manage vast amounts of user data, enabling advanced analytics and personalized recommendations.
Examples
- https://medium.com/@MarinAgli1/learning-apache-iceberg-storing-the-data-to-minio-s3-56670cef199d