Category: Learning Levels

This post explores how BMW implemented AWS Lake Formation’s fine-grained access control (FGAC) in the Cloud Data Hub and how this saves them up to 25% on compute and storage costs. By using AWS Lake Formation fine-grained access control capabilities, BMW has transparently implemented finer data access management within the Cloud Data Hub. The integration of Lake Formation has enabled data stewards to scope and grant granular access to specific subsets of data, reducing costly data duplication.

Amazon Redshift now offers enhanced query performance with optimizations such as Enhanced Histograms for Selectivity Estimation in the absence of fresh statistics by relying on metadata statistics gathered during ingestion. In this post, we cover new performance optimizations in Redshift data warehouse query processing and how elastic histogram statistics help enhance selectivity estimation and the overall quality of query plans for Amazon Redshift data warehouse queries in the absence of fresh table statistics.

This post is co-written with Matt Vogt from Immuta.  Organizations are looking for products that let them spend less time managing data and more time on core business functions. Data security is one of the key functions in managing a data warehouse. With Immuta integration with Amazon Redshift, user and data security operations are managed […]

Amazon Redshift has introduced a new feature called the Query profiler. The Query profiler is a graphical tool that helps users analyze the components and performance of a query. This feature is part of the Amazon Redshift console and provides a visual and graphical representation of the query’s run order, execution plan, and various statistics. The Query profiler makes it easier for users to understand and troubleshoot their queries. In this post, we cover two common use cases for troubleshooting query performance. We show you step-by-step how to analyze and troubleshoot long-running queries using the Query profiler.

Adoption of data lakes and the data mesh framework emerges as a powerful approach. By decentralizing data ownership and distribution, enterprises can break down silos and enable seamless data sharing. In this post, we discuss how to choose the right tool for building an enterprise data platform and enabling data sharing, collaboration and access within your organization and with third-party providers. We address three business use cases using AWS Glue, AWS Data Exchange, AWS Clean Rooms, and Amazon DataZone through three different use cases.

In this post, we walk you through how to configure service provider-initiated authentication for OpenSearch Dashboards by using OpenSearch Service and Keycloak. We also discuss how to set up users, groups, and roles in Keycloak and configure their access to OpenSearch Dashboards.

We’re thrilled to announce the launch of the official Amazon OpenSearch Service YouTube channel—a comprehensive resource for anyone looking to master Amazon OpenSearch Service. Whether you’re just getting started with searches , vectors, analytics, or you’re looking to optimize large-scale implementations, our channel can be your go-to resource to help you unlock the full potential of OpenSearch Service.

In this post, we demonstrate the critical role of metadata in text-to-SQL generation through an example implemented for Amazon Athena using Amazon Bedrock. We discuss the challenges in maintaining the metadata as well as ways to overcome those challenges and enrich the metadata.

Starting with the Amazon EMR 7.2 release, Amazon EMR on EC2 introduced a new feature called Application Master (AM) label awareness, which allows users to enable YARN node labels to allocate the AM containers within On-Demand nodes only. In this post, we explore the key features and use cases where this new functionality can provide significant benefits, enabling cluster administrators to achieve optimal resource utilization, improved application reliability, and cost-efficiency in your EMR on EC2 clusters.

This post provides a detailed walkthrough about how to efficiently capture and manage manual snapshots in OpenSearch Service. It covers the essential steps for taking snapshots of your data, implementing safe transfer across different AWS Regions and accounts, and restoring them in a new domain. This guide is designed to help you maintain data integrity and continuity while navigating complex multi-Region and multi-account environments in OpenSearch Service.