AWS Big Data Blog

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.

In this post, you learn how to use Data Agent in Query Editor to explore data, build multi-step analyses, recover from errors, and summarize results using a public education dataset.

In this post, we walk you through the new scheduling and orchestrating capabilities for notebooks in Amazon SageMaker Unified Studio.

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.

In this post, we walk through how Zynga adopted Amazon Redshift federated permissions and AWS IAM Identity Center to enforce consistent, tiered data access across provisioned and serverless Amazon Redshift environments without building custom synchronization pipelines.

In this post, we show you how to tackle data discovery, classification, and governance across your databases, data warehouses, and object storage to regain visibility and control over your data landscape.

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.