Category: Intermediate (200)

Amazon Redshift system table integration with Amazon S3 Tables automatically delivers your system table logs to Amazon S3 Tables in Apache Iceberg format. You can retain this data well beyond the 7-day limit for compliance, auditing, and cross-warehouse observability, without custom ETL pipelines or cluster resource consumption.

Learn how to track Amazon SageMaker Unified Studio project costs by custom tags. This serverless solution enriches AWS Cost and Usage Report (CUR) data with custom project tags and visualizes cost by CostCenter, Team, or Environment in an Amazon Quick Sight dashboard.

Agentic AI applications now observe, reason, and act on streaming data in production. This post presents three architecture patterns that form a unified streaming backbone for the agentic AI era: streaming feature engineering with real-time inference, event-driven agent invocation, and real-time context synchronization.

Learn how to deploy an open-source, AI-powered agent built on Amazon Bedrock that automatically analyzes every Amazon Kinesis Data Streams stream in your account, compares costs across the three capacity modes, and recommends the optimal mode to help you save over 60% on streaming costs on a schedule you choose.

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.