Category: Storage

In this post, we discuss how Taxbit partnered with Amazon Web Services (AWS) to streamline their crypto tax analytics solution using Amazon S3 Tables, achieving 82% cost savings and five times faster processing times.

In this post, we discuss the best practices that you can follow while querying Apache Iceberg data with Amazon Redshift

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.

In this post, we show you how Medidata created a unified, scalable, real-time data platform that serves thousands of clinical trials worldwide with AWS services, Apache Iceberg, and a modern lakehouse architecture.

In this post, we walk you through the new capabilities in Iceberg V3, explain how deletion vectors and row lineage address these challenges, explore real-world use cases across industries, and provide practical guidance on implementing Iceberg V3 features across AWS analytics, catalog, and storage services.

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, 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.

In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. You’ll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation.

AWS recently announced that Amazon SageMaker now offers Amazon Simple Storage Service (Amazon S3) based shared storage as the default project file storage option for new Amazon SageMaker Unified Studio projects. This feature addresses the deprecation of AWS CodeCommit while providing teams with a straightforward and consistent way to collaborate on project files across the […]

This blog post discusses how to create a seamless integration between Amazon SageMaker Lakehouse and Snowflake for modern data analytics. It specifically demonstrates how organizations can enable Snowflake to access tables in AWS Glue Data Catalog (stored in S3 buckets) through SageMaker Lakehouse Iceberg REST Catalog, with security managed by AWS Lake Formation. The post provides a detailed technical walkthrough of implementing this integration, including creating IAM roles and policies, configuring Lake Formation access controls, setting up catalog integration in Snowflake, and managing data access permissions. While four different patterns exist for accessing Iceberg tables from Snowflake, the blog focuses on the first pattern using catalog integration with SigV4 authentication and Lake Formation credential vending.