Best practices for querying Apache Iceberg data with Amazon Redshift

This article provides best practices for querying Apache Iceberg data with Amazon Redshift to optimize performance and costs.

• Follow table design best practices by selecting appropriate data types for storage and query efficiency
• Partition Iceberg tables on frequently-used filter columns to enable partition pruning and reduce data scans
• Select only necessary columns instead of using SELECT * to reduce resource utilization and costs
• Generate AWS Glue Data Catalog column-level statistics for better query optimization by the cost-based optimizer
• Implement table maintenance strategies including compaction, snapshot expiration, and unreferenced file removal
• Create incremental materialized views on Iceberg tables to accelerate dashboard query performance
• Use late binding views to encapsulate business logic and improve query optimization and data security
• Consider using Amazon S3 Tables for automated management of compaction and maintenance tasks

These practices help achieve optimal query performance, reduced costs, and efficient resource utilization when working with Iceberg tables in Redshift.