In the ever-evolving landscape of data management, it’s time to break the ice and dive deep into Apache Iceberg, a revolutionary open table format that’s making waves. Just as an iceberg reveals only a fraction of its true mass above water, Apache Iceberg brings powerful, yet often unseen, capabilities to the forefront, offering a scalable and efficient solution for managing petabytes of data. Whether you’re navigating through a data lake or setting sail towards a data lakehouse, Apache Iceberg promises to keep your data well-structured and accessible, preventing your operations from hitting any hidden obstacles beneath the surface.
But What is Apache Iceberg?
Apache Iceberg is an open-source high-performance format for large analytic tables. It enables the use of tables for big data while making it possible for engines like Spark, Trino, Flink, Presto, Hive, Impala, Pig, and others to safely work with the same tables, at the same time. Iceberg is released under the Apache License, which means that the software is open source and comes with certain freedoms and responsibilities. Details on Apache License 2.0 can be found here.
As an open source format for data, storing data in Apache Iceberg format makes it easier to expose to multiple tools than data stored in a traditional, proprietary format. Furthermore, Apache Iceberg is designed to handle large datasets efficiently.
The Benefits of Apache Iceberg
Apache Iceberg provides features that are designed to increase the speed and efficiency of querying data, including fast scan planning, pruning metadata files that are not needed, and the ability to filter out data files that do not contain matching data.
Automatic partitioning is another benefit of Iceberg. As such, users need not know the structural layout of files in their table before running queries on them. Iceberg hides the table partitioning making it unnecessary to specify extra query filters. Furthermore, with Iceberg you can change the partitioning scheme of your data without rewriting existing data.
Another benefit of Apache Iceberg is that it supports complex schema evolution without requiring a rewrite of the data. Making changes like adding, dropping, renaming, or reordering columns can be accomplished without having to perform costly and perhaps disruptive operations.
Apache Iceberg’s historical data access capability offers time travel, enabling users to query historical versions of their data. This can be essential for auditing, debugging, and even comparing current results against past data without having to restore old data.
Another feature that is beneficial for auditing is Iceberg’s ability to track changes and data lineage. Tools for auditing are important for data stewards to ensure compliance with regulatory requirements and data lineage allows analysts to understand the origin and transformation of their data.
Iceberg maintains snapshots of data, which can be used to roll back to previous states, view changes over time, or experiment with different analytical models. This is akin to version control in software, allowing data users to confidently experiment without fear of losing work or data integrity.
Furthermore, Apache Iceberg provides ACID compliance, meaning that users can rely on consistent and accurate results from their queries, even in concurrent environments.
Handling large amounts of data is another hallmark of Apache Iceberg. It is designed to scale efficiently with large datasets, making it possible for users to perform analytics on petabytes of data without worrying about performance degradation.
Use Cases
Apache Iceberg is a versatile open table format so it’s primary use cases span several critical areas in modern data management and analytics.
Perhaps the first, most common use case, is for data lakes and data lakehouses. Apache Iceberg can play a key role in the data lakehouse architecture, which aims to combine the flexibility of data lakes with the performance and structure of data warehouses. It provides ACID transactions, schema evolution, and efficient querying on top of data lakes, making it possible to run complex analytics directly on raw data stored in object storage systems like Amazon S3, Azure Blob Storage, or Google Cloud Storage.
Another strong use case for Iceberg is to unify batch and streaming workloads. Iceberg supports both batch and streaming data processing, making it suitable for environments where real-time and historical data need to be analyzed together. It integrates with stream processing engines like Apache Flink and batch processing engines like Apache Spark, enabling seamless handling of large-scale, time-sensitive data.
Iceberg’s ability to handle large datasets with fine-grained control over data versions and partitions makes it well-suited for multi-tenancy environments. Organizations can manage datasets for different teams or customers within a single platform, ensuring data is isolated and managed according to specific needs.
Enabling high performance for Machine Learning workflows is another potent use case for Apache Iceberg. It’s efficient data storage and retrieval capabilities make Iceberg an excellent choice for supporting machine learning workflows, where large volumes of data need to be ingested, processed, and analyzed quickly. It integrates well with tools like Apache Spark, allowing data scientists to perform advanced analytics and feature engineering directly on data stored in a data lakehouse.
Data migration and ETL processes are another use case where Iceberg excels: Iceberg’s robust transaction support and schema evolution capabilities make it an excellent choice for ETL processes in complex data environments.
Organizations looking to modernize their data infrastructure can use Iceberg to migrate from traditional databases and data warehouses to a more flexible and scalable data lake or lakehouse architecture. Iceberg’s compatibility with multiple processing engines and its support for schema evolution make it easier to transition existing workloads without significant disruptions.
These use cases highlight Apache Iceberg’s role as a powerful, flexible tool for managing large-scale datasets in modern data environments, particularly where scalability, flexibility, and performance are crucial.
A Bit of History
Iceberg was originally developed by engineers at Netflix (particularly by Ryan Blue, Dan Weeks, and others) to address the limitations of existing table formats like Apache Hive. Hive was used by many different services and engines in the Netflix infrastructure. Hive was never able to guarantee correctness and did not provide stable atomic transactions. So, Netflix needed a more robust, scalable, and reliable table format as they dealt with petabyte-scale datasets stored in Apache Hive tables on Amazon S3.
In late 2018, Netflix released Apache Iceberg as an open-source project, contributing it to the broader data engineering community. The project was designed to be engine-agnostic, meaning it could work with different processing engines like Apache Spark, Apache Flink, and others.
After its release, Iceberg quickly gained attention from other tech companies facing similar challenges with data lakes and large-scale data management. Companies like Apple, Expedia, and Adobe began adopting Iceberg, contributing to its development and helping to build a community around it.
In November 2019, Iceberg entered the Apache Software Foundation (ASF) Incubator. This step marked its official recognition as a project with potential to become a top-level Apache project. During this period, the community worked on improving the software, adding features, and ensuring it adhered to Apache’s standards.
In May 2020, Apache Iceberg graduated from the Apache Incubator to become a top-level Apache project. This graduation signified that Iceberg had matured in terms of community engagement, codebase stability, and adoption, positioning it as a critical component of modern data lake architectures.
Over the years, Iceberg has integrated with major data processing engines like Apache Spark, Apache Flink, Trino, Presto, and others. This has made it a versatile choice for organizations looking to implement a unified table format across various big data platforms. Some of the major vendors currently supporting Apache Iceberg tables in their products include AWS, Cloudera, Databricks, Google Cloud Platform, IBM, Microsoft Azure, Snowflake, Starburst, and many others.
Today, Iceberg is used by a wide range of companies across different industries, from technology and media to finance and retail. Its flexibility and scalability have made it a go-to solution for managing large datasets in cloud-based and on-premises data lakes.
IBM Support for Apache Iceberg
Since I predominantly work supporting and managing data using IBM offerings I will briefly outline the support IBM offers support for the Apache Iceberg format:
- IBM watsonx.data®: This fit-for-purpose data store, built on an open data lakehouse architecture, allows you to create tables in the Apache Iceberg format. It ensures compatibility with various query engines and prevents vendor lock-in.
- IBM Db2 Database, Db2 Warehouse, Netezza, and Informix: These databases offer integrations with watsonx.data and support for open formats like Apache Iceberg. This unifies data and metadata across hybrid cloud environments without the need for migration or re-cataloging.
- Db2 Warehouse: It fully supports open formats such as Parquet, Avro, ORC, and the Iceberg table format. This enables data sharing and insights extraction across teams without additional ETL.
- Netezza: With recent support for open formats like Parquet and Apache Iceberg, Netezza empowers data engineers, scientists, and analysts to run complex workloads without additional data movement or ETL.
The Bottom Line
As the data management landscape evolves, Apache Iceberg is expected to play a significant role in the future of data lakehouse architectures, offering a robust and scalable solution for organizations handling increasingly large and complex datasets.
Apache Iceberg addresses many of the limitations of older table formats, providing a robust, flexible, and efficient solution for managing large-scale data in modern analytics environments.
