Category: Technical How-to

This post explores how to select the right workflow orchestration service based on your specific use case requirements. We’ll examine key workflow characteristics, present real-world scenarios, and provide practical guidance to help you make an informed decision for your particular needs.

In this post, we show you how to build a CDC pipeline that delivers query-ready Iceberg tables directly. The pipeline captures inserts, updates, and deletes from Aurora PostgreSQL and applies them as row-level operations in Amazon S3 Tables, a capability of Amazon Simple Storage Service (Amazon S3).

In this post, we walk through a hands-on PySpark migration from Spark 3.5 to Spark 4.0 on Amazon EMR Serverless, using the AWS Spark Upgrade Agent. You’ll see how the agent iteratively validates your application on a live Amazon EMR Serverless application, automatically diagnosing and resolving failures from Amazon CloudWatch logs until the job succeeds.

This post shows you how to migrate your JMS applications and walks through a complete setup, from creating the broker to sending and receiving messages. You will also see a real-world scenario: migrating an existing Apache ActiveMQ workload to an Amazon MQ broker running RabbitMQ. The post covers configuration changes, monitoring with Amazon CloudWatch, and validation steps to make sure that your migration succeeds.

In this post, you learn how to set up Kiro with the Amazon Redshift MCP server to query your data warehouse using natural language. You explore cluster discovery, schema browsing, analytical queries, cross-cluster comparisons, and data quality checks, all without writing SQL from scratch or switching between tools.

In a previous post, we showed you how to query Amazon SageMaker Catalog metadata using SQL by using the metadata export feature. This post builds on that foundation by demonstrating how to create governance dashboards with Amazon Quick.

In this post, you learn how to use Data Agent in Query Editor to explore data, build multi-step analyses, recover from errors, and summarize results using a public education dataset.

In this post, we walk you through the new scheduling and orchestrating capabilities for notebooks in Amazon SageMaker Unified Studio.

This post offers an overview of the security mechanisms available for Amazon OpenSearch Service, spanning authentication and authorization, encryption, and network access controls. You learn how to implement fine-grained access control, manage AWS Identity and Access Management (IAM) roles, and secure data both in transit and at rest for both public and virtual private cloud (VPC) access domains.

In this post, you’ll walk through a practical, step-by-step example that shows how to capture and track data lineage from Spark jobs running on Amazon EMR directly into Amazon SageMaker Catalog using OpenLineage. You’ll see how lineage metadata flows automatically and explore data relationships and dependencies across your workflows in Amazon SageMaker Unified Studio.