Category: Amazon SageMaker Data & AI Governance

In this post, we demonstrate how to develop and monitor a Spark application using existing data in Amazon S3 using SageMaker Unified Studio. The solution addresses key challenges organizations face in managing big data analytics workloads through an integrated development environment where data teams can develop, test, and refine Spark applications while leveraging EMR Serverless for dynamic resource allocation and built-in monitoring tools.

This post provides guidance on how you can use event driven architecture to enhance the resiliency of data solutions built on the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI. SageMaker is a managed service with high availability and durability.

In this blog post, we will demonstrate how business units can use Amazon SageMaker Unified Studio to discover, subscribe to, and analyze these distributed data assets. Through this unified query capability, you can create comprehensive insights into customer transaction patterns and purchase behavior for active products without the traditional barriers of data silos or the need to copy data between systems.