New AWS Solutions Implementation: SQL-based ETL with Apache Spark on Amazon EKS
You can now set up SQL-Based ETL with Apache Spark on Amazon EKS. This solutions implementation provides declarative data processing support, codeless extract-transform-load (ETL) capabilities, and workflow orchestration automation to help data scientists and analysts access their data and create meaningful insights without the need for manual IT processes.
You can now set up SQL-Based ETL with Apache Spark on Amazon EKS. This solutions implementation provides declarative data processing support, codeless extract-transform-load (ETL) capabilities, and workflow orchestration automation to help data scientists and analysts access their data and create meaningful insights without the need for manual IT processes.
This solution abstracts common ETL activities, including formatting, partitioning, and transforming datasets, into configurable and productive data processes. This abstraction results in actionable insights that is derived quickly to help you accelerate your data-driven business decisions. Additionally, the solution uses an open-source Arc data processing framework and powered by Apache Spark and container technologies to simplify Spark application development and deployment.
To learn more and get started, please visit the solutions implementation web page.
AWS Solutions Implementations help you solve common problems and build faster using the AWS platform. Additional AWS Solutions Implementations are available on the AWS Solutions Implementations web page, where you can browse technical reference implementations that are vetted by AWS architects, offering detailed architecture and instructions for deployment.