Harness powerful insights from your data with SQL-Based ETL with Apache Spark on Amazon EKS

This Guidance helps address the gap between data consumption requirements and low-level data processing activities performed by common ETL practices. For organizations operating on SQL-based data management systems, adapting to modern data engineering practices can slow down the progress of harnessing powerful insights from their data. This Guidance provides a quality-aware design for increasing data process productivity through the open-source data framework Arc for a user-centered ETL approach. The Guidance accelerates interaction with ETL practices, fostering simplicity and raising the level of abstraction for unifying ETL activities in both batch and streaming.

AWS also offers options for an optimal design using AWS Graviton-based instances that allow you to optimize the performance and cost of running ETL jobs at scale on Amazon EKS.

Learn more at: https://aws.amazon.com/solutions/guidance/sql-based-etl-with-apache-spark-on-amazon-eks/