AWS Big Data Blog

If you’re struggling with manual data classification in your organization, the new Amazon SageMaker Catalog AI agent can automate this process for you. Most large organizations face challenges with the manual tagging of data assets, which doesn’t scale and is unreliable. In some cases, business terms aren’t applied consistently across teams. Different groups name and tag data assets based on local conventions. This creates a fragmented catalog where discovery becomes unreliable and governance teams spend more time normalizing metadata than governing. In this post, we show you how to implement this automated classification to help reduce the manual tagging effort and improve metadata consistency across your organization.

(Continued from Part 1) In this post, we show how you can give on-premises clients and spoke account resources private access to OpenSearch Serverless collections distributed across multiple business unit accounts.

In this post, we show how organizations can provide secure, private access to multiple Amazon OpenSearch Serverless collections from both on-premises environments and distributed AWS accounts using a single centralized interface VPC endpoint and Route 53 Profiles.

In this post, you learn how to set up an automated, end-to-end solution that extracts tables from Amazon Aurora MySQL Serverless v2 and writes them to Amazon S3 Tables in Apache Iceberg format using AWS Glue.

In this post, we show you how Amazon Managed Streaming for Apache Kafka (Amazon MSK) Express brokers brokers streamline the end-to-end activities for Kafka administration.

Finding the right data assets in large enterprise catalogs can be challenging, especially when thousands of datasets are cataloged with organization-specific metadata. Amazon SageMaker Unified Studio now supports custom metadata search filters. In this post, you learn how to create custom metadata forms, publish assets with metadata values, and use structured filters to discover those assets.

While working with Lambda User-Defined Functions (UDFs) in Amazon Redshift, knowing best practices may help you streamline the respective feature development and reduce common performance bottlenecks and unnecessary costs. You wonder what programming language could improve your UDF performance, how else can you use batch processing benefits, what concurrency management considerations might be applicable in your case? In this post, we answer these and other questions by providing a consolidated view of practices to improve your Lambda UDF efficiency. We explain how to choose a programming language, use existing libraries effectively, minimize payload sizes, manage return data, and batch processing. We discuss scalability and concurrency considerations at both the account and per-function levels. Finally, we examine the benefits and nuances of using external services with your Lambda UDFs.

In this post, Vanguard’s Financial Advisor Services division describes how they evolved from a single Amazon Redshift cluster to a multi-warehouse architecture using data sharing and serverless endpoints to eliminate performance bottlenecks caused by exponential growth in ETL jobs, dashboards, and user queries.

This post provides a comprehensive technical walkthrough for implementing Amazon Redshift federated permissions with AWS IAM Identity Center to help achieve scalable data governance across multiple data warehouses. It demonstrates a practical architecture where an Enterprise Data Warehouse (EDW) serves as the producer data warehouse with centralized policy definitions, helping automatically enforce security policies to consuming Sales and Marketing data warehouses without manual reconfiguration.

Growing data volume, variety, and velocity has made it crucial for businesses to implement architectures that efficiently manage and analyze data, while maintaining data integrity and consistency. In this post, we show you a solution that combines Apache Iceberg, Data Build Tool (dbt), and Amazon EMR to create a scalable, ACID-compliant transactional data lake. You can use this data lake to process transactions and analyze data simultaneously while maintaining data accuracy and real-time insights for better decision-making.