You Will be Surprised by the Performance of Apache Iceberg on Snowflake

AUDIENCE: Technical

LEVEL: Basic

Apache Iceberg continues to grow in popularity as the industry standard for open table formats.

Iceberg’s robust ecosystem of diverse adopters, contributors, and commercial options helps prevent storage lock-in. This eliminates the need to transfer or duplicate tables across systems — resulting in reduced compute and storage costs for your entire data stack.

In this article, we will discuss the performance of Apache Iceberg on Snowflake. We will also compare benchmark results that measure how Iceberg performs to that of Parquet-backed external tables and Snowflake’s native tables.

How Fast is Iceberg on Snowflake? A Benchmark Analysis

We all understand that organizations need storage formats that deliver both flexibility and speed. Snowflake expanded its ecosystem to support open table formats like Apache Iceberg. Snowflake put Iceberg through a series of benchmarks to compare its performance against other formats commonly used on the platform, such as Parquet-backed external tables and native Snowflake tables. Let’s dive into how these setups were tested and what the results reveal about Iceberg’s capabilities.

Benchmark Setup: A Fair Comparison

The team designed the benchmark to test Iceberg’s capabilities under real-world conditions, comparing it with other storage options in Snowflake:

1. Parquet-Backed External Tables on S3: This configuration adhered to Snowflake’s best practices for file and row group sizes, simulating a popular storage method on AWS S3.
2. Iceberg Tables on S3 with AWS Glue Catalog: By using Iceberg with AWS Glue for metadata, Snowflake evaluated Iceberg’s potential as a high-performance format.
3. Iceberg Tables on External S3 with Snowflake Catalog: This setup stored Iceberg tables on an external S3 volume, using the Snowflake catalog for metadata management, which made it easier to leverage Snowflake’s ecosystem.
4. Snowflake Native Tables: As a benchmark, Snowflake native tables served as a baseline to measure how well Iceberg and Parquet could compete with Snowflake’s optimized storage.

Each configuration was tested with a set of queries, from basic filters to complex joins, to gauge performance across different use cases.

The Results: Iceberg’s Standout Performance

Iceberg’s performance exceeded expectations:

• Iceberg on S3 (AWS Glue catalog or Snowflake catalog) was consistently over 25 times faster than Parquet-backed external tables. The boost in query speed was evident across diverse query types, demonstrating Iceberg’s strength in handling complex data workloads.
• Snowflake Native Tables remained highly performant, but Iceberg’s performance came closer than ever, especially with optimized queries.

These results show that Iceberg can rival even Snowflake’s native storage performance, providing a flexible and fast solution for teams seeking open table formats.

Why Iceberg Shines on Snowflake

The impressive performance of Iceberg is due to several key features:

• Efficient Partitioning and Pruning: Iceberg’s architecture allows it to minimize data scanned in queries by using advanced partition pruning, which is invaluable for large datasets.
• Big Data-Optimized: Designed for analytics at scale, Iceberg leverages fast read capabilities that make it a natural fit for big data environments.
• Flexible Metadata Management: Iceberg works with both AWS Glue and Snowflake’s catalog, providing flexibility for organizations to integrate with their existing metadata management tools.

Key Takeaways

The benchmark confirms that Iceberg is a powerful option on Snowflake for organizations that want both fast query performance and the flexibility of open formats. For those looking to maximize analytics speed on Snowflake while leveraging open table standards, Iceberg emerges as a compelling choice.

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