Category: Advanced (300)

AWS recently announced the general availability of GPU-accelerated vector (k-NN) indexing on Amazon OpenSearch Service. You can now build billion-scale vector databases in under an hour and index vectors up to 10 times faster at a quarter of the cost. This feature dynamically attaches serverless GPUs to boost domains and collections running CPU-based instances. With […]

AWS Glue zero-ETL with SAP now supports data ingestion and replication from SAP data sources such as Operational Data Provisioning (ODP) managed SAP Business Warehouse (BW) extractors, Advanced Business Application Programming (ABAP), Core Data Services (CDS) views, and other non-ODP data sources. Zero-ETL data replication and schema synchronization writes extracted data to AWS services like Amazon Redshift, Amazon SageMaker lakehouse, and Amazon S3 Tables, alleviating the need for manual pipeline development. In this post, we show how to create and monitor a zero-ETL integration with various ODP and non-ODP SAP sources.

This post shows how Amazon EMR 7.12 can make your Apache Spark and Iceberg workloads up to 4.5x faster performance.

In this post, we analyze the results from our benchmark tests comparing the Amazon EMR 7.9 optimized Spark runtime against Spark 3.5.5 without encryption optimizations. We walk through a detailed cost analysis and provide step-by-step instructions to reproduce the benchmark.

AWS Glue now supports catalog federation for remote Iceberg tables in the Data Catalog. With catalog federation, you can query remote Iceberg tables, stored in Amazon S3 and cataloged in remote Iceberg catalogs, using AWS analytics engines and without moving or duplicating tables. In this post, we discuss how to get started with catalog federation for Iceberg tables in the Data Catalog.

In this post, we show how you can use Amazon Redshift to write data directly to Apache Iceberg tables stored in Amazon S3 and S3 Tables for seamless integration between your data warehouse and data lake while maintaining ACID compliance.

In this post, we show how to use the new visual workflow experience in SageMaker Unified Studio IAM-based domains to orchestrate an end-to-end machine learning workflow. The workflow ingests weather data, applies transformations, and generates predictions—all through a single, intuitive interface, without writing any orchestration code.

In this post, you learn how Amazon Redshift Serverless Reservations can help you lower your data warehouse costs. We explore ways to determine the optimal number of RPUs to reserve, review example scenarios, and discuss important considerations when purchasing these reservations.

Amazon SageMaker Catalog now supports custom metadata forms and rich text descriptions at the column level, extending existing curation capabilities for business names, descriptions, and glossary term classifications. Column-level context is essential for understanding and trusting data. This release helps organizations improve data discoverability, collaboration, and governance by letting metadata stewards document columns using structured and formatted information that aligns with internal standards. In this post, we show how to enhance data discovery in SageMaker Catalog with custom metadata forms and rich text documentation at the schema level.

In this post, you learn how to integrate SageMaker Unified Studio with S3 Tables and query your data using Amazon Athena, Amazon Redshift, or Apache Spark in EMR and AWS Glue.