Category: Intermediate (200)

Agent Skills for Amazon MSK bring broker-type-aware expertise to operating and migrating Apache Kafka clusters. In this post, we walk through installing the managing-amazon-msk and migrate-to-msk skills and demonstrate how they diagnose performance issues, size clusters with cost breakdowns, and plan migrations from self-managed Kafka to Amazon MSK.

In this post, we show you how to set up the agent for Amazon EMR on EKS and walk through troubleshooting a failed job run. We demonstrate the workflow from both the Amazon EMR console and an AI assistant that supports the Model Context Protocol (MCP), an open standard for connecting AI assistants to external tools and data.

Amazon EMR Serverless now supports a 32 vCPU / 244 GB worker configuration for the most demanding Spark jobs. Across 126 TPC-DS and TPC-H queries, larger workers delivered an average 29% faster query execution and 29% lower cost, with the biggest gains on shuffle-heavy, multi-table join queries.

In this post, you build a contract compliance search system that combines semantic search with semantic highlighting in Amazon OpenSearch Service. You deploy the solution using two AWS CloudFormation stacks, test it with synthetic contract documents, and see how a single query surfaces both the right contracts and the right clauses within them.

In this post, we show how Apache Celeborn resolves this trade-off for Amazon EMR on EKS and Amazon EMR on EC2, improving job reliability while unlocking additional cost savings.

Apache Spark Connect bridges the gap between these two worlds: you develop in local Python, but execute on AWS Glue against actual data. Today, AWS Glue interactive sessions support Spark Connect natively. You can connect from any environment that supports the PySpark remote() API, including VS Code, PyCharm, Amazon SageMaker Unified Studio notebooks, and standalone Python applications. You don’t need to install specialized kernels or manage cluster infrastructure.

In this post, we explore how MDAA transforms data architecture development from months of manual coding to production-ready deployment through configuration-driven infrastructure and embedded governance, examine a real customer transformation, and provide a clear implementation pathway for your own data modernization journey.

In this post, we describe the innovations that make RG instances so much faster. We also share benchmark results showing that RG delivers up to 4.2x better price-performance than other leading data warehouses.

In this post, we introduce new capabilities of Amazon Redshift that enhance our multi-warehouse and scaling capabilities: remote materialized view (MV) operations, remote table DDL support, and concurrency scaling enhancements for zero-ETL and S3 event integration. These features help you build more scalable, performant decentralized analytics architectures on Amazon Redshift.

In this post, we provide a guide to help you use Tableau’s Relationships and Amazon Redshift Serverless architecture to deliver sub-second insights while maximizing every Redshift Processing Unit (RPU). We also provide guidance on five key areas: data model architecture for optimal query performance, security configuration and access control, performance optimization through smart configuration, cost management strategies, and query and join optimization techniques.