Category: Learning Levels

In this post, we demonstrate how to launch a high availability instance fleet cluster using the newly redesigned Amazon EMR console, as well as using an AWS CloudFormation template. We also go over the basic concepts of Hadoop high availability, EMR instance fleets, the benefits and trade-offs of high availability, and best practices for running resilient EMR clusters.

In this post, we show you how to manage user access to enterprise documents in generative AI-powered tools according to the access you assign to each persona. This post illustrates how to build a document search RAG solution that makes sure only authorized users can access and interact with specific documents based on their roles, departments, and other relevant attributes. It combines OpenSearch Service and Amazon Cognito custom attributes to make a tag-based access control mechanism that makes it straightforward to manage at scale.

By harnessing the capabilities of generative AI, you can automate the generation of comprehensive metadata descriptions for your data assets based on their documentation, enhancing discoverability, understanding, and the overall data governance within your AWS Cloud environment. This post shows you how to enrich your AWS Glue Data Catalog with dynamic metadata using foundation models (FMs) on Amazon Bedrock and your data documentation.

This second post of a two-part series that details how Volkswagen Autoeuropa, a Volkswagen Group plant, together with AWS, built a data solution with a robust governance framework using Amazon DataZone to become a data-driven factory. Part 1 of this series focused on the customer challenges, overall solution architecture and solution features, and how they helped Volkswagen Autoeuropa overcome their challenges. This post dives into the technical details, highlighting the robust data governance framework that enables ease of access to quality data using Amazon DataZone.

In this post, we show how to use Amazon Kinesis Data Streams to buffer and aggregate real-time streaming data for delivery into Amazon OpenSearch Service domains and collections using Amazon OpenSearch Ingestion. You can use this approach for a variety of use cases, from real-time log analytics to integrating application messaging data for real-time search. In this post, we focus on the use case for centralizing log aggregation for an organization that has a compliance need to archive and retain its log data.

This post focuses on introducing an active-passive approach using a snapshot and restore strategy. The snapshot and restore strategy in OpenSearch Service involves creating point-in-time backups, known as snapshots, of your OpenSearch domain. These snapshots capture the entire state of the domain, including indexes, mappings, and settings. In the event of data loss or system failure, these snapshots will be used to restore the domain to a specific point in time. The post walks through the steps to set up this disaster recovery solution, including launching OpenSearch Service domains in primary and secondary regions, configuring snapshot repositories, restoring snapshots, and failing over/failing back between the regions.

Amazon Redshift now provides the ability to incrementally refresh your materialized views on data lake tables including open file and table formats such as Apache Iceberg. In this post, we will show you step-by-step what operations are supported on both open file formats and transactional data lake tables to enable incremental refresh of the materialized view.

In this post, we discuss how to implement fine-grained access control in EMR Serverless using Lake Formation. With this integration, organizations can achieve better scalability, flexibility, and cost-efficiency in their data operations, ultimately driving more value from their data assets.

In this post, we discuss how Volkswagen Autoeuropa used Amazon DataZone to build a data marketplace based on data mesh architecture to accelerate their digital transformation. The data mesh, built on Amazon DataZone, simplified data access, improved data quality, and established governance at scale to power analytics, reporting, AI, and machine learning (ML) use cases. As a result, the data solution offers benefits such as faster access to data, expeditious decision making, accelerated time to value for use cases, and enhanced data governance.

Amazon DataZone now launched authentication support through the  Amazon Athena JDBC driver, allowing data users to seamlessly query their subscribed data lake assets via popular business intelligence (BI) and analytics tools like Tableau, Power BI, Excel, SQL Workbench, DBeaver, and more. This integration empowers data users to access and analyze governed data within Amazon DataZone using familiar tools, boosting both productivity and flexibility.