Introduction to Apache Iceberg

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What is Apache Iceberg?

Apache Iceberg is an open source table format for large-scale analytics. It improves upon the limitations of traditional table storage solutions by offering a high-performance, more efficient way of managing data at scale. Iceberg allows for fine-grained control over data, enabling features such as schema evolution, time travel, and transactional support, which are crucial for modern data architectures.

Netflix originally launched the Iceberg project to address the challenges of handling its massive data warehouse and to improve the performance and scalability of existing table formats. Subsequently, it was open sourced and has since been adopted by a wide range of companies, such as Airbnb, Adobe, LinkedIn, and many more.

Apache Iceberg benefits

Optimized query performance

Apache Iceberg uses several different techniques to improve performance. Here’s an overview of some of the primary methods:

  • File partitioning - Data can be partitioned on multiple fields, including complex nested data structures. This can help reduce the amount of data that queries need to scan.
  • Predicate pushdown - By supporting predicate pushdowns, Iceberg enables data to be filtered at the storage level without requiring query engines to load the data into memory, reducing data transfer and processing time.
  • Incremental scans - Iceberg enables incremental scans, which only read the part(s) of a file that changed since its last query. This helps improve performance for repetitive or frequent queries by reducing the data to be reprocessed.
  • Compaction - Iceberg automatically compacts and resizes files to reduce fragmentation and unnecessary access.

Metadata management

Iceberg provides centralized metadata management, simplifying data governance and management by maintaining a consistent view of large datasets. This approach ensures that metadata is always up-to-date and accessible, reducing the complexity and overhead of managing data at scale.

Big data ecosystem integrations

Apache Iceberg works seamlessly with popular data processing frameworks such as Apache Spark, Apache Flink, and Presto. This compatibility lets developers ‌integrate Iceberg into their existing data pipelines without significant changes, allowing for easier adoption and implementation.

Schema evolution support

One of the key features of Apache Iceberg is its support for efficient schema evolution. It allows for additions, deletions, and updates to the schema of a table without disrupting existing data, ensuring that data remains accessible and queryable even as the underlying schema changes.

ACID transaction support

Iceberg brings ACID guarantees to big data, enabling transactional support for large datasets. This feature ensures data integrity and consistency across multiple operations, a crucial aspect for applications requiring strict data accuracy and reliability. Its importance is particularly pronounced in large organizations where numerous systems may be simultaneously querying or updating data.

Apache Iceberg use cases

Data lakes

Apache Iceberg is ideal for data lakes, offering a structured and efficient way to manage vast amounts of raw data. Its features support scalable data storage, optimized query performance, and seamless schema evolution, making it suitable for enterprises to leverage their data lakes for analytical insights.

Data lakehouses

Iceberg can also be used to create data lakehouses by blending the best features of data warehouses and data lakes. It provides support for the ACID transactions and performance optimizations expected in a data lakehouse architecture.

Data governance

With its centralized metadata management and transactional support, Apache Iceberg plays a crucial role in data governance. It ensures data integrity, compliance, and security across all data operations, making it a valuable tool for organizations looking to implement effective data governance strategies.

Apache Iceberg key concepts and features

Apache Iceberg introduces several key concepts and features designed to significantly enhance how data is managed and accessed for analytics. Here’s a deeper look into some of these features

Table metadata

Iceberg tracks tables by maintaining a tree of metadata files that hold information about tables and partitions. Iceberg maintains a catalog that points to these files and acts as a metadata layer separate from the underlying data layer. Table metadata allows Iceberg to have features like snapshots, time travel, and atomic transaction support.


Iceberg’s partitioning system is a dynamic and highly configurable feature that enhances query performance by organizing data into more manageable chunks based on specific column values. Unlike traditional static partitioning, Iceberg supports partitioning on multiple columns and allows custom partitioning strategies, such as bucketing or range partitioning. This flexibility enables users to tailor the partitioning scheme to their specific query patterns, significantly reducing the amount of data scanned during queries.

Data can be partitioned by date for time series analysis, ensuring that queries for specific time periods are more efficient. Iceberg’s partitioning system evolves without requiring a rewrite of the dataset, facilitating easier maintenance and adjustments as query patterns change.

Time travel

Time travel in Iceberg enables users to query data as it existed at a specific time, offering significant benefits for compliance, auditing, and historical data analysis. This feature is made possible by Iceberg’s snapshot management, which maintains a history of table states at different points in time. Users can easily revert to or query these snapshots, allowing for analysis of historical data, auditing changes over time, or recovering from accidental data modifications or deletions.

Time travel facilitates a range of use cases, from tracking the evolution of data over time for trend analysis to conducting forensic investigations following incidents. By providing a straightforward way to access historical data states, Iceberg empowers organizations to meet compliance requirements, conduct in-depth data analysis, and ensure data integrity and reliability.

Companies using Apache Iceberg


The Adobe Experience Platform team uses Iceberg as part of their data lake architecture, which enables customers to build real-time personalized experiences. Adobe’s platform uses a lambda architecture to process petabytes of data for customers, partners, and internal users. Adobe migrated to Iceberg after outgrowing their internal solution, which attempted to solve similar problems.


Netflix created Iceberg internally to solve problems with Apache Hive. These problems included issues with query correctness, an inability to provide stable atomic transactions, difficulty changing data formats, and poor write performance. Netflix realized many other companies had similar issues and donated Iceberg to the Apache Foundation in 2018. It graduated to a top-level project in 2020.


Airbnb adopted Apache Iceberg as part of their migration from a legacy data warehouse. The original data warehouse used Hive as the metastore, which became a performance bottleneck as the number of partitions increased for their data. This required aggregating data and reducing retention periods, limiting insights from data analysis. Airbnb addressed these issues by migrating from Hive to Iceberg.