Category: Technical How-to

In this post, we show you how to unify governance and metadata across Amazon SageMaker Unified Studio and Atlan through a comprehensive bidirectional integration. You’ll learn how to deploy the necessary AWS infrastructure, configure secure connections, and set up automated synchronization to maintain consistent metadata across both platforms.

In this post, we demonstrate how to implement Apache Spark Connect on Amazon EMR on Amazon Elastic Compute Cloud (Amazon EC2) to build decoupled data processing applications. We show how to set up and configure Spark Connect securely, so you can develop and test Spark applications locally while executing them on remote Amazon EMR clusters.

In this post, we show how to create and update Iceberg tables with partitions in the Data Catalog using the AWS SDK and AWS CloudFormation.

With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose.

As we witness the gradual transition from IPv4 to IPv6, AWS continues to expand its support for dual-stack networking across its service portfolio. In this post, we show how you can migrate your Amazon Redshift Serverless workgroup from IPv4-only to dual-stack mode, so you can make your data warehouse future ready.

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.