Category: Intermediate (200)

Today, AWS is announcing support for Spark Connect on Amazon EMR Serverless with EMR release 7.13 (Apache Spark 3.5.6) and later versions. You can now build and debug Spark applications from your preferred local environment while running full-scale Spark operations on EMR Serverless.

With this general availability announcement, Spark 4.0 is now supported across Amazon EMR Serverless, Amazon EMR on EC2, and Amazon EMR on EKS deployment options. In this post, you’ll learn about key Spark 4.0 capabilities now available on Amazon EMR including Spark Connect, the Variant data type, SQL scripting, Python API improvements, and streaming enhancements, along with infrastructure changes in the new emr-spark-8.0 release.

In this post, you learn how to set up Kiro with the Amazon Redshift MCP server to query your data warehouse using natural language. You explore cluster discovery, schema browsing, analytical queries, cross-cluster comparisons, and data quality checks, all without writing SQL from scratch or switching between tools.

In a previous post, we showed you how to query Amazon SageMaker Catalog metadata using SQL by using the metadata export feature. This post builds on that foundation by demonstrating how to create governance dashboards with Amazon Quick.

This post offers an overview of the security mechanisms available for Amazon OpenSearch Service, spanning authentication and authorization, encryption, and network access controls. You learn how to implement fine-grained access control, manage AWS Identity and Access Management (IAM) roles, and secure data both in transit and at rest for both public and virtual private cloud (VPC) access domains.

In this post, you’ll walk through a practical, step-by-step example that shows how to capture and track data lineage from Spark jobs running on Amazon EMR directly into Amazon SageMaker Catalog using OpenLineage. You’ll see how lineage metadata flows automatically and explore data relationships and dependencies across your workflows in Amazon SageMaker Unified Studio.

Today, we are announcing a ground-up re-architecture of Amazon OpenSearch Serverless that delivers up to 20 times faster autoscaling, scale to zero, and up to 60% lower cost than provisioning clusters for peak load. Amazon OpenSearch Service is a fully managed, open source retrieval engine that unifies vector, lexical, hybrid, and agentic search, delivering low-latency, accurate and relevant results. Amazon OpenSearch Serverless is an automatically scaled deployment option. The new architecture decouples compute from storage. The service provisions infrastructure in seconds instead of minutes, and scales compute all the way to zero when your application is idle. In this post, we walk through the new architecture, what it means for your applications, and how to get started with a hands-on tutorial.

In this post, we explore how Buildkite uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and Amazon Managed Service for Apache Flink to power Test Engine’s streaming-first analytics architecture at scale.

Enterprises face challenges when teams create data assets outside of central data catalogs. It adds overhead for discovery, and limits collaboration. Amazon’s Business Data Technologies (BDT) team has built an enterprise data catalog Andes for sharing datasets under well-defined policies. However, teams created catalog of local datasets and other non-tabular assets such as dashboards and metrics, outside Andes. This made it difficult to discover all assets in a consolidated way. In this post, we share how Amazon.com is working to integrate catalogs by extending enterprise data catalog Andes with Amazon SageMaker.

In this post, we walk through how to set up and manage S3 Tables in the AWS Glue Data Catalog, create and query Iceberg materialized views, and configure access controls that work across your analytics stack with IAM-based authorization.