Apache Iceberg

Why Traditional Data Architecture Falls Short—and How Apache Iceberg Can Help

Traditional data architecture patterns come with significant limitations. These outdated methods often require Extract, Transform, Load (ETL) processes to move data into each tool, a costly and cumbersome approach that leads to data silos and drift. Moreover, this practice locks your data into specific proprietary tools and formats. Fortunately, there’s a better way, and this book will show you how.

Apache Iceberg offers a modern solution that delivers the capabilities, performance, scalability, and cost-efficiency needed for an open data lakehouse. By applying the concepts in this book, you’ll be able to handle interactive, batch, machine learning, and streaming analytics without the need to duplicate data across various proprietary systems and formats.

What is Apache Iceberg?

Apache Iceberg is a high-performance table format designed for massive analytic tables. It brings the reliability and simplicity of SQL tables to big data, allowing engines like Spark, Trino, Flink, Presto, Hive, and Impala to work with the same tables concurrently and safely.

Here’s what makes Iceberg stand out:

Expressive SQL: Iceberg supports advanced SQL commands for merging new data, updating rows, and deleting records. It can optimize read performance by eagerly rewriting data files or use delete deltas for quicker updates.
Full Schema Evolution: Schema changes are seamless. You can add, rename, or reorder columns without rewriting the entire table. No more “zombie” data or complex schema updates.
Hidden Partitioning: Iceberg automates partitioning, eliminating the need for manual filtering. It efficiently skips unnecessary partitions and files, adapting the table layout as data and queries evolve.
Time Travel and Rollback: Time-travel capabilities allow reproducible queries with specific table snapshots, and version rollback lets users revert tables to previous states to correct issues quickly.
Data Compaction: Iceberg supports out-of-the-box data compaction with various strategies, such as bin-packing or sorting, to optimize file layout and size.

Comparing Apache Iceberg with Other Technologies

Iceberg vs. Parquet: Iceberg excels in large-scale data warehousing and real-time processing, while Parquet is known for its integration with various big data tools and its focus on query performance and storage efficiency.
Iceberg vs. Hive: Unlike Hive Metastore, where changes are managed through Hive alone, Iceberg allows multiple tools to concurrently update tables, providing a complete history of schema and data changes.

What Problems Does Apache Iceberg Solve?

Apache Iceberg simplifies building data lakes and performing data operations for anyone familiar with SQL. It ensures data consistency, meaning that any user accessing the data will see a unified view.

Is Apache Iceberg a Lakehouse?

Yes, Apache Iceberg is the open table format at the heart of the data lakehouse architecture. Its detailed metadata files and analytics-optimized design enhance query engine efficiency.

Iceberg and Snowflake

Iceberg Tables combine the performance and familiar query capabilities of Snowflake tables with customer-managed cloud storage. This integration helps Snowflake users overcome common barriers and unlock the full value of their data.