Category: Technical How-to

In this post, we show how to use Amazon SageMaker Catalog to publish data from multiple sources, including Amazon S3, Amazon Redshift, and Snowflake. This approach enables self-service access while ensuring robust data governance and metadata management.

In this post, we show you how the Apache Spark troubleshooting agent helps analyze Apache Spark issues by providing detailed root causes and actionable recommendations. You’ll learn how to streamline your troubleshooting workflow by integrating this agent with your existing monitoring solutions across Amazon EMR and AWS Glue.

In this post, you learn how to assess your existing Amazon EMR Spark applications, use the Spark upgrade agent directly from the Kiro IDE, upgrade a sample e-commerce order analytics Spark application project (including build configs, source code, tests, and data quality validation), and review code changes before rolling them out through your CI/CD pipeline.

AWS Glue zero-ETL with SAP now supports data ingestion and replication from SAP data sources such as Operational Data Provisioning (ODP) managed SAP Business Warehouse (BW) extractors, Advanced Business Application Programming (ABAP), Core Data Services (CDS) views, and other non-ODP data sources. Zero-ETL data replication and schema synchronization writes extracted data to AWS services like Amazon Redshift, Amazon SageMaker lakehouse, and Amazon S3 Tables, alleviating the need for manual pipeline development. In this post, we show how to create and monitor a zero-ETL integration with various ODP and non-ODP SAP sources.

This post shows how Amazon EMR 7.12 can make your Apache Spark and Iceberg workloads up to 4.5x faster performance.

In this post, we walk you through the new capabilities in Iceberg V3, explain how deletion vectors and row lineage address these challenges, explore real-world use cases across industries, and provide practical guidance on implementing Iceberg V3 features across AWS analytics, catalog, and storage services.

In this post, we show how you can use Amazon Redshift to write data directly to Apache Iceberg tables stored in Amazon S3 and S3 Tables for seamless integration between your data warehouse and data lake while maintaining ACID compliance.

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.