Apache Iceberg
I. Introduction
Product Name: Apache Iceberg
Brief Description: Apache Iceberg is an open-source table format for huge analytic tables. It brings the reliability and simplicity of SQL tables to big data while enabling multiple engines to safely work with the same tables concurrently.
II. Project Background
- Library/Framework: Apache Software Foundation
- Authors: Netflix (original creators)
- Initial Release: 2017
- Type: Open-source table format for big data
- License: Apache License 2.0
III. Features & Functionality
- Open Table Format: Supports multiple engines (Spark, Trino, Flink, Presto, Hive, Impala, etc.) to access the same table.
- Schema Evolution: Allows for schema changes without rewriting the entire table.
- Time Travel: Enables querying historical data versions.
- Data Skipping: Optimizes query performance by skipping unnecessary data files.
- Partition Evolution: Allows for changes in table partitioning without rewriting data.
- Hidden Partitioning: Automatically handles partition creation and management.
IV. Benefits
- Data Lake Reliability: Provides a consistent and reliable foundation for data lakes.
- Improved Query Performance: Optimizes query execution through data skipping and efficient storage layout.
- Simplified Data Management: Enables easier schema evolution and data versioning.
- Increased Flexibility: Supports multiple data processing engines and tools.
- Openness: Benefits from a large and active community.
V. Use Cases
- Data Lakes: Building and managing large-scale data lakes.
- Data Warehousing: Creating data warehouses with efficient query performance.
- Machine Learning: Training and serving machine learning models with large datasets.
- Data Integration: Combining data from various sources into a unified table.
VI. Applications
- Financial services
- Telecommunications
- Retail
- Adtech
- IoT
VII. Getting Started
- Integrate Iceberg into your data processing pipelines (e.g., Apache Spark, Trino).
- Create Iceberg tables and load data.
- Utilize Iceberg’s features for schema evolution, time travel, and query optimization.
VIII. Community
- Apache Iceberg Website: https://iceberg.apache.org/
- Apache Iceberg GitHub: https://github.com/apache/iceberg
IX. Additional Information
- Compatible with various data processing frameworks and tools.
- Supports multiple data formats (Parquet, Avro, ORC).
- Active community and ecosystem of tools and libraries.
X. Conclusion
Apache Iceberg is a high-performance table format that simplifies data management in big data environments. Its open format, schema evolution capabilities, and query optimization features make it a popular choice for building data lakes and data warehouses.