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Engineering
The Ultimate Resource Hub for Optimizing Iceberg Tables
Optimizing Lakehouse Tables
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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
- General best practices
- Optimizing read performance
- Optimizing write performance
- Optimizing storage
- Maintaining tables by using compaction
- Using Iceberg workloads in Amazon S3
Check out our GitHub repository for more resources on optimizing lakehouse tables.
Frequently asked questions (FAQs)
How do I integrate e6data with my existing data infrastructure?
We are universally interoperable and open-source friendly. We can integrate across any object store, table format, data catalog, governance tools, BI tools, and other data applications.
How does billing work?
We use a usage-based pricing model based on vCPU consumption. Your billing is determined by the number of vCPUs used, ensuring you only pay for the compute power you actually consume.
What kind of file formats does e6data support?
We support all types of file formats, like Parquet, ORC, JSON, CSV, AVRO, and others.
What kind of performance improvements can I expect with e6data?
e6data promises a 5 to 10 times faster querying speed across any concurrency at over 50% lower total cost of ownership across the workloads as compared to any compute engine in the market.
What kinds of deployment models are available at e6data ?
We support serverless and in-VPC deployment models.
How does e6data handle data governance rules?
We can integrate with your existing governance tool, and also have an in-house offering for data governance, access control, and security.
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