Category: Technical How-to

In this post, we demonstrate an automated test suite that validates your Amazon Redshift cluster automatically after any patch, reboot, or modification. It uses standard drivers against real workload patterns to provide a verified gate between a patch landing and that patch reaching production.

In this post, I show you how writable warm storage removes the costly migration cycle. You can reduce your infrastructure costs by up to 48 percent and update historical data in seconds instead of hours. I walk through a real-world cost comparison and performance benchmarks, and help you decide when to use writable warm versus UltraWarm.

In this post, you learn how to build an application log pipeline for production use with Amazon CloudWatch Logs, AWS Lambda, Amazon Data Firehose, AWS Glue, and Apache Iceberg materialized tables. You then use materialized views to accelerate query performance. This solution helps you achieve faster query response times on large-scale log data without requiring you to manage continuous data lake refresh.

This post shows how developers, data engineers, and analysts can connect to a secure Spark Connect endpoint in Athena with Apache Spark. You can use your preferred tools, such as Jupyter notebooks, VS Code, or dbt with Apache Airflow, without managing cluster lifecycle or scaling.

In this post, you learn how to build an AI-powered solution that collects the telemetry, pre-computes performance signals, correlates them with CloudWatch, and uses Amazon Bedrock to generate prioritized recommendations.

In this post, we show you how to diagnose multi-layer Medallion Architecture pipeline failures in minutes using AWS DevOps Agent with Apache Spark Troubleshooting Agent integrated as an MCP server.

In this post, we explain how Private Networking for Amazon MQ for RabbitMQ works and walk through the setup process. Whether you’re securing a private identity provider, federating messages between brokers, or connecting to self-hosted RabbitMQ, your broker can now reach private destinations without exposing them publicly.

In this post, we demonstrate reading from and writing to Lake Formation-managed S3 locations using Apache Spark jobs from EMR. Lake Formation credential vending for S3 location access is available in EMR release label 7.13 and later, Boto3 1.42.29 and later, AWS Java SDK 2.41.32 and later, and AWS Command Line Interface (AWS CLI) version 2.33.1 and later.

In this post, we explore how to build an online shopping AI agent. We focus on its architecture and implementation with Amazon OpenSearch Service, Amazon Bedrock AgentCore, and Strands Agents. Amazon Bedrock AgentCore is an agentic platform for deploying and operating those agents and tools securely at scale without managing infrastructure.

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.