Category: Intermediate (200)

A repeatable process for keeping Apache HBase on Amazon EMR highly available. Part 1 covers triaging an HBase coordinator that is slow to start, a RegionServer that leaves the cluster, and regions that stay in transition, with the web UI pages, commands, and log signatures that point to each cause.

The next generation of Amazon OpenSearch Service serverless now supports time series collections. Define the boundaries for cost, performance, security, access, and data retention using simple controls for data tiering, time field selection, and data retention, then turn raw events into insights with the next-generation OpenSearch UI.

The Amazon OpenSearch Service Search Relevance Agent turns a low NDCG score into a better search query in minutes. Describe the problem through the Ask AI chat, and the agent analyzes your User Behavior Insights data, generates an improved query, and validates it with an offline Search Relevance Workbench experiment, all without leaving the UI.

Moeve standardized how it runs dbt across multiple data lakes by building a centralized, serverless launcher on Amazon Athena, AWS Step Functions, AWS Fargate, Amazon DynamoDB, and Amazon EventBridge, cutting new-project onboarding from days to about 15 minutes while keeping compute close to the data and orchestration loosely coupled.

Connect Microsoft Power BI directly to governed data in Amazon SageMaker Unified Studio using new authentication modes in the Amazon Athena ODBC driver, with no third-party ODBC-JDBC bridge. Part 1 covers IAM Identity Center (IDC)-based domains with both DSN-based and DSN-less connection methods.

Connect Microsoft Power BI directly to governed data in Amazon SageMaker Unified Studio using the Amazon Athena ODBC driver. Part 2 covers IAM-based domains with SageMakerIam authentication, including AWS IAM Identity Center administrator setup, for both DSN-based and DSN-less connection methods.

Amazon Kinesis Data Streams now supports streaming tables, a fully managed capability that continuously delivers your streaming data as queryable Apache Iceberg tables on Amazon S3 Tables. Streaming tables reduce data delivery costs to S3 Tables by up to 50% compared to self-managed alternatives and reduce downstream query costs by up to 30% through intelligent inline compaction that eliminates the small file problem. You need no custom applications, no self-managed compute, and no operational overhead.

You can now use PythonOperator and BashOperator to run custom Python functions and shell scripts directly in the Amazon MWAA Serverless runtime, without provisioning additional infrastructure. This post walks through building a serverless pipeline that converts CSV files to JSON using a PythonOperator and verifies the output with a BashOperator.

Amazon EMR on EKS now runs Apache Spark up to 3.7x faster on Amazon EC2 G7 instances with NVIDIA RTX PRO 4500 Blackwell GPUs than on comparable CPU instances, with no changes to existing Spark code. See the TPC-DS benchmark results, the cost comparison, and how to get started.

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.