Engineering
The Ultimate Resource Hub for Optimizing Iceberg Tables
By Karthic Rao, Fredson Lewis
At e6data, we are big admirers of Apache Iceberg. We're witnessing a steep increase in its adoption, with our customers running E6data's query engine for heavy workloads.
While we were scrambling for resources on the internet to optimize Iceberg, why not curate it for the rest of the community?
Here's a curated collection of links, guides, and insights to help you discover the best practices for optimizing your Iceberg tables.
- Optimization Strategies for Iceberg Tables by Cloudera
- Compaction in Apache Iceberg: Fine-Tuning Your Iceberg Table’s Data Files by Dremio
- Improving performance with Iceberg sorted tables by Starburst
- Partitioning and Indexing in Apache Iceberg by IOMETE
- Optimizing read performance by AWS
- Maintaining tables by using compaction by AWS
- Iceberg 101: A Guide to Iceberg Partitioning by Upsolver
- Iceberg Tables Optimization by Upsolver
- How Z-Ordering in Apache Iceberg Helps Improve Performance by Dremio
- Z-ORDER sorting during compaction by IOMETE
- Iceberg 101: Ten Tips to Optimize Performance by Upsolver
- Optimizing Iceberg tables by AWS
- https://iceberg.apache.org/docs/1.6.0/performance/ Apache Iceberg official documentation
- Manage and Optimize Iceberg tables for efficient data storage and querying by AWS
- Best practices for optimizing Apache Iceberg workloads by AWS
Check out our GitHub repository for more resources on optimizing lakehouse tables.
Frequently asked questions
Does the hub link to official Apache Iceberg performance documentation?
Yes. It includes a direct link to the Apache Iceberg “performance” documentation page.
Are AWS-specific best-practice guides included?
Yes. The list features several AWS documents on general best practices, optimizing reads and writes, storage tuning, compaction, and running Iceberg workloads on Amazon S3.
Is there a recommended guide focused on Iceberg partitioning?
Yes. The hub links to “Iceberg 101: A Guide to Iceberg Partitioning,” which concentrates entirely on effective partition design.
Where can I find additional lakehouse optimization materials beyond the article?
A GitHub repository curated by e6data is linked at the end of the post for further exploration.