Iceberg and other Data Warehousing Technologies

The world of data warehousing is evolving rapidly, with organizations handling ever-growing datasets and seeking tools that balance performance, flexibility, and cost. Let's talk about the different data warehousing technologies to support data lakes and data fabric operations. Among the many options available today, Apache Iceberg has been gaining attention for its innovative approach. Let's take a closer look at Iceberg’s unique features and contrasts it with popular alternatives like Amazon Redshift and Snowflake.

Apache Iceberg: The Basics

Apache Iceberg is an open table format designed to simplify and improve the way organizations manage large analytic datasets. Its appeal lies in its flexibility and the fact that it integrates with a range of existing tools, including Apache Spark, Kafka, Flink, and Hive. This means businesses already working with these technologies can incorporate Iceberg without overhauling their systems. Its design is also intuitive, often described as "pythonic," making it accessible for developers and data teams familiar with Python-based workflows.

Why Iceberg Stands Out

One of Iceberg’s standout features is its hidden partitioning. Unlike traditional approaches where users must manually define and manage partitions, Iceberg automates this process. This is a big win for teams, as it reduces the complexity of data management and minimizes human error. Another major strength is its cloud-agnostic nature. Iceberg can work with any object storage—whether it’s Amazon S3, Google Cloud Storage, Azure Blob Storage, or even on-premises systems like Hadoop Distributed File System (HDFS). Iceberg enables the use of object storage while decoupling compute, allowing organizations to scale resources independently and optimize costs. This flexibility offers organizations freedom from vendor lock-in and the ability to adopt multi-cloud strategies, which are increasingly common.

Additionally, Iceberg supports schema evolution, allowing users to make changes to the data model—like adding or renaming columns—without breaking existing queries or rewriting historical data. Other features like time travel for querying historical snapshots and support for ACID transactions further enhance its appeal.

How Iceberg Compares to Redshift and Snowflake

Of course, Iceberg isn’t the only option for data warehousing. Managed services like Amazon Redshift and Snowflake remain go-to choices for many organizations, and for good reason. Amazon Redshift is a petabyte-scale, fully managed data warehouse designed for performance. It’s tightly integrated with the AWS ecosystem, making it a natural fit for businesses already using AWS services - like S3, Lambda, and Glue, providing a unified analytics experience. Redshift is optimized for SQL-based analytics, offering fast query performance using columnar storage and massively parallel processing (MPP).

However, it requires explicit partitioning and lacks the flexibility of Iceberg’s schema evolution and open architecture. Additionally, its compute and storage aren’t fully decoupled, which can sometimes lead to cost inefficiencies.

Snowflake, on the other hand, has made a name for itself with its simplicity and multi-cloud support. Unlike Redshift, Snowflake works across AWS, Azure, and Google Cloud, giving users more flexibility. It’s also designed to scale compute and storage independently, which helps optimize costs. Snowflake has advanced features like built-in support for semi-structured data (e.g., JSON), data sharing, and cloning that make Snowflake highly versatile. That said, Snowflake uses a proprietary data format, which can lead to concerns about vendor lock-in. While Snowflake excels as a managed platform, it lacks the open, integration-friendly nature, which is critical for organizations seeking flexibility and long-term control over their data.

Opinion: Where Iceberg Fits

Ultimately, all of these technologies are great - it just depends on your use case and budget. All offer enterprise data warehousing solutions that can scale to what organizations need now and will need in the future. In many ways, Iceberg feels like a response to the challenges of traditional data warehousing tools. Its open architecture, cloud-agnostic design, and seamless integration with widely used technologies make it particularly appealing for organizations looking to modernize their data infrastructure. That said, it’s not a managed solution like Snowflake or Redshift, so teams adopting Iceberg should be prepared to invest in managing and maintaining their setup.  For businesses that value flexibility and control—particularly those already using Apache tools—Iceberg offers a compelling alternative. It’s not a one-size-fits-all solution, but it’s a thoughtful approach to data warehousing that’s well-suited to the demands of modern, cloud-native data strategies.