If you manage data in Amazon Aurora MySQL-Compatible Edition and want to make it available for analytics, machine learning (ML), or cross-service querying in a modern lakehouse format, you’re not alone.
Organizations often need to run analytics, build ML models, or join data across multiple sources. These are examples of workloads that can be resource-intensive and impractical to run directly against a transactional database. By extracting your Aurora MySQL data into Amazon S3 Tables in Apache Iceberg format, you can offload analytical queries from your production database without impacting its performance, while storing data in a fully managed Iceberg table store optimized for analytics. Built on the open Apache Iceberg standard, Amazon Simple Storage Service (Amazon S3) Table data is queryable from engines like Amazon Athena, Amazon Redshift Spectrum, and Apache Spark without additional data copies. You can also combine relational data with other datasets already…
