AWS Big Data Blog

Integrate custom applications with AWS Lake Formation – Part 1

In this two-part series, we show how to integrate custom applications or data processing engines with Lake Formation using the third-party services integration feature. In this post, we dive deep into the required Lake Formation and AWS Glue APIs. We walk through the steps to enforce Lake Formation policies within custom data applications. As an example, we present a sample Lake Formation integrated application implemented using AWS Lambda.

Integrate custom applications with AWS Lake Formation – Part 2

In this two-part series, we show how to integrate custom applications or data processing engines with Lake Formation using the third-party services integration feature. In this post, we explore how to deploy a fully functional web client application, built with JavaScript/React through AWS Amplify (Gen 1), that uses the same Lambda function as the backend. The provisioned web application provides a user-friendly and intuitive way to view the Lake Formation policies that have been enforced.

Manage access controls in generative AI-powered search applications using Amazon OpenSearch Service and Amazon Cognito

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.

Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

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.

How FINRA established real-time operational observability for Amazon EMR big data workloads on Amazon EC2 with Prometheus and Grafana

FINRA performs big data processing with large volumes of data and workloads with varying instance sizes and types on Amazon EMR. Amazon EMR is a cloud-based big data environment designed to process large amounts of data using open source tools such as Hadoop, Spark, HBase, Flink, Hudi, and Presto. In this post, we talk about our challenges and show how we built an observability framework to provide operational metrics insights for big data processing workloads on Amazon EMR on Amazon Elastic Compute Cloud (Amazon EC2) clusters.

Your guide to AWS Analytics at AWS re:Invent 2024

It’s AWS re:Invent time, where you turn your ideas into reality. Get a front row seat to hear real stories from AWS customers, experts and leaders about navigating pressing topics like generative AI and data analytics. For data enthusiasts and data professionals alike, this blog is a curated and comprehensive guide to all analytics sessions, for you to efficiently plan your itinerary.

Ingest telemetry messages in near real time with Amazon API Gateway, Amazon Data Firehose, and Amazon Location Service

These organizations use third-party satellite-powered terminal devices for remote monitoring using telemetry and NMEA-0183 formatted messages generated in near real time. This post demonstrates how to implement a satellite-based remote alerting and response solution on the AWS Cloud to provide time-critical alerts and actionable insights, with a focus on telemetry message ingestion and alerts. Key services in the solution include Amazon API Gateway, Amazon Data Firehose, and Amazon Location Service.

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Expand data access through Apache Iceberg using Delta Lake UniForm on AWS

Delta Lake UniForm is an open table format extension designed to provide a universal data representation that can be efficiently read by different processing engines. It aims to bridge the gap between various data formats and processing systems, offering a standardized approach to data storage and retrieval. With UniForm, you can read Delta Lake tables as Apache Iceberg tables. This post explores how to start using Delta Lake UniForm on Amazon Web Services (AWS).

How Volkswagen Autoeuropa built a data solution with a robust governance framework, simplifying access to quality data using Amazon DataZone

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.

Streamlining AWS Glue Studio visual jobs: Building an integrated CI/CD pipeline for seamless environment synchronization

As data engineers increasingly rely on the AWS Glue Studio visual editor to create data integration jobs, the need for a streamlined development lifecycle and seamless synchronization between environments has become paramount. Additionally, managing versions of visual directed acyclic graphs (DAGs) is crucial for tracking changes, collaboration, and maintaining consistency across environments. This post introduces an end-to-end solution that addresses these needs by combining the power of the AWS Glue Visual Job API, a custom AWS Glue Resource Sync Utility, and an based continuous integration and continuous deployment (CI/CD) pipeline.