Category: Advanced (300)

Learn how to observe and evaluate production AI agents by combining an agent running on AWS with OpenSearch Agent Health. This post walks through deploying an agent and its observability pipeline to AWS, then using Agent Health to explore traces and run evaluations that measure and improve agent quality over time.

Extend AWS DevOps Agent to investigate Apache Spark failures on Amazon EMR. This post shows how to register the Apache Spark Troubleshooting Agent for Amazon EMR as a custom MCP capability provider over AWS PrivateLink, so a single agent chat session diagnoses a failing Spark job from an Amazon CloudWatch alarm to a line-numbered root cause.

Amazon Redshift now supports AWS IAM Identity Center authentication on clusters and workgroups that use enhanced VPC routing. Create two interface VPC endpoints to give your users single sign-on with their corporate credentials while keeping all authentication traffic on the AWS private network.

With AWS Glue 6.0, you can build real-time, near-real-time, and batch data pipelines on a single platform. Using a financial market-risk example, learn how to flag high-risk trades with sub-second latency using Spark Real-Time Mode, store heterogeneous pricing vectors with Apache Iceberg v3 Variant columns, and run batch analytics with Arrow-native UDFs.

Picnic runs RabbitMQ as the messaging backbone for hundreds of microservices on Amazon MQ for RabbitMQ. This post shows how to configure one broker to trust multiple OAuth 2.0 identity providers, Keycloak for operators and AWS IAM for services, so you can eliminate static credentials while maintaining separate identity paths for people and workloads.

AWS Glue 6.0 with Apache Spark 4.1 adds support for Apache Iceberg v3: native geospatial types, nanosecond-precision timestamps, the VARIANT type, and DEFAULT column values. This post builds a connected vehicle fleet telemetry pipeline that uses all four in a single Iceberg v3 table, from ingestion through spatial, nanosecond, and variant queries.

Your Google BigQuery users need to query data that lives in Amazon S3 Tables on AWS without copying it across clouds. This post shows how to connect BigQuery to Amazon S3 Tables through the AWS Glue Iceberg REST Catalog using IAM-based access control, so you keep one governed dataset and query it live from BigQuery.

In Part 2 of this series, connect Google BigQuery to Amazon S3 Tables using AWS Lake Formation credential vending. Lake Formation manages fine-grained permissions and issues short-lived, scoped credentials to external engines, so you can centrally govern which teams and query engines read your Iceberg tables on AWS without managing IAM policies for every consumer.

AWS Glue 6.0 is now available, lowering AWS Glue pricing by 30%, adding an AWS optimized build of Apache Spark 4.1, and introducing Apache Iceberg V3 capabilities suitable for enterprise adoption. This post covers the key capabilities and performance benefits, with code examples to help you get started.

Walk through upgrading a PySpark ETL job from AWS Glue 5.1 to AWS Glue 6.0 using the generative AI upgrades for Apache Spark. The upgrade analysis automatically detects incompatibilities, applies fixes, and validates results with data quality checks.