Capabilities

Apache Iceberg is an open-source data table format designed for enabling high-performance querying, managing, and sharing of large datasets across various data storage systems. It was created to address the limitations of traditional data storage formats for large-scale data analytics use cases. Apache Iceberg provides features like schema evolution, time travel, and ACID (Atomicity, Consistency, Isolation, Durability) transactions, making it suitable for data warehousing, data lake scenarios, and other big data processing tasks. The format allows for efficient queries on data stored in various file systems, cloud storage services, and distributed data processing engines, promoting data portability and compatibility across different platforms.

Key Features:

• Schema Evolution: Iceberg supports evolving the schema of data tables without breaking existing queries or requiring expensive batch rewrite operations.
• Time Travel: Users can query data at different points in time, allowing for historical analysis without the need for complex data versioning.
• ACID Transactions: Iceberg provides transactional guarantees, ensuring that data remains consistent even in the presence of failures or concurrent writes.
• Table Metadata: Iceberg stores rich metadata, including schema, partitioning scheme, and snapshot history, making it easier to manage and query data.
• Partitioning: It supports various partitioning strategies to organize data for efficient querying, reducing data scan sizes.
• Compression and Encoding: Iceberg optimizes storage by using columnar compression and encoding techniques, improving query performance and storage efficiency.
• Pluggable Storage: It supports multiple storage backends, including HDFS, AWS S3, and more, offering flexibility in choosing the right storage for your needs.

Use Cases:

1. Data Warehousing: Iceberg is suitable for building data warehouses that require efficient querying, schema evolution, and historical analysis capabilities.
2. Data Lakes: It can be used to manage and organize large-scale data lakes, providing a structured way to store data while allowing for easy query and access.
3. Batch Analytics: Iceberg is beneficial for running batch analytics on large datasets, offering performance optimizations through columnar storage and partitioning.
4. Streaming Data: It can handle data streams by providing mechanisms to periodically append new data while maintaining schema and historical data integrity.
5. Historical Analysis: Iceberg's time travel feature enables users to analyze data changes over time, making it valuable for compliance reporting and auditing.

Apache Iceberg aims to provide a reliable and efficient solution for managing large-scale data while maintaining data quality, query performance, and schema evolution capabilities. It is widely used in big data processing scenarios where data integrity and scalability are critical.