Category: Intermediate (200)

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.

In this post, we outline the solution that provides cross-Region resiliency without needing to reestablish relationships during a fail-back, using an active-active replication model with Amazon OpenSearch Ingestion (OSI) and Amazon Managed Streaming for Apache Kafka (Amazon MSK). This solution applies to both OpenSearch Service managed clusters and Amazon OpenSearch Serverless collections. We use Amazon OpenSearch Serverless as an example for the configurations in this post.

We’re happy to announce that Amazon OpenSearch Ingestion pipelines can now read from S3 buckets in different Regions to ingest and consolidate data into a single OpenSearch Service domain or collection. In this post, I’ll show you how to use the new cross-Region support to ingest data from S3 buckets across multiple AWS Regions into a single OpenSearch Service domain or collection.

In this post, we discuss how you can use Precisely Connect to enable real-time, direct replication of mainframe data to Amazon Simple Storage Service (Amazon S3), and how your organization can extend this foundation using Amazon S3 Tables for advanced analytics.