Category: Technical How-to

In this post, we show how to use the new visual workflow experience in SageMaker Unified Studio IAM-based domains to orchestrate an end-to-end machine learning workflow. The workflow ingests weather data, applies transformations, and generates predictions—all through a single, intuitive interface, without writing any orchestration code.

In this post, you learn how to integrate SageMaker Unified Studio with S3 Tables and query your data using Amazon Athena, Amazon Redshift, or Apache Spark in EMR and AWS Glue.

In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. You’ll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation.

Today, AWS announced Amazon Managed Workflows for Apache Airflow (MWAA) Serverless. This is a new deployment option for MWAA that eliminates the operational overhead of managing Apache Airflow environments while optimizing costs through serverless scaling. In this post, we demonstrate how to use MWAA Serverless to build and deploy scalable workflow automation solutions.

In this post, you’ll learn how to build this comprehensive monitoring solution step-by-step. You’ll gain practical experience designing an event-driven pipeline, implementing data processing workflows, and creating insightful dashboards that help you track interruption trends, optimize ASG configurations, and improve the resilience of your Spot Instance workloads.

Amazon SageMaker now enhances search results in Amazon SageMaker Unified Studio with additional context that improves transparency and interpretability. The capability introduces inline highlighting for matched terms and an explanation panel that details where and how each match occurred across metadata fields such as name, description, glossary, and schema. In this post, we demonstrate how to use enhanced search in Amazon SageMaker.

In this post, we provide step-by-step instructions to set up Amazon EMR on EC2, EMR Serverless, and AWS Glue within SageMaker Unified Studio, enabled with trusted identity propagation. We use the setup to illustrate how different IAM Identity Center users can run their Spark sessions, using each compute setup, within the same project in SageMaker Unified Studio. We show how each user will see only tables or part of tables that they’re granted access to in Lake Formation.

This post shows step-by-step guidance to setup workforce access to Amazon SageMaker Unified Studio using Okta as an external Identity provider with AWS IAM Identity Center.

Organizations need to efficiently manage data assets while maintaining governance controls in their data marketplaces. Although manual approval workflows remain important for sensitive datasets and production systems, there’s an increasing need for automated approval processes with less sensitive datasets. In this post, we show you how to automate subscription request approvals within SageMaker, accelerating data access for data consumers.

On February 6th 2025, AWS introduced fine-grained access control based on AWS Lake Formation for EMR on EKS from Amazon EMR 7.7 and higher version. You can now significantly enhance your data governance and security frameworks using this feature. In this post, we demonstrate how to implement FGAC on Apache Iceberg tables using EMR on EKS with Lake Formation.