Category: Amazon Simple Storage Service (S3)

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.

We now have a public preview of two integrations between Amazon Simple Storage Service (Amazon S3) Vectors and Amazon OpenSearch Service that give you more flexibility in how you store and search vector embeddings. In this post, we walk through this seamless integration, providing you with flexible options for vector search implementation.

In this post, we explore how Amazon S3 Tables has expanded its automatic compaction capabilities to include Avro and ORC file formats for Apache Iceberg tables, alongside the previously supported Parquet format. Through performance testing with over 20 billion events, the capability demonstrates significant query performance improvements ranging from 12% to 40% when using compacted tables compared to non-compacted tables across different file formats.

In this post, we show you how Stifel implemented a modern data platform using AWS services and open data standards, building an event-driven architecture for domain data products while centralizing the metadata to facilitate discovery and sharing of data products.

In this post, we walk through two solutions that demonstrate how to stream data from your Amazon MSK provisioned cluster to Iceberg-based data lakes in Amazon S3 using Amazon Data Firehose.

In this post, we demonstrate how you can bring transactional data from AWS OLTP data stores like Amazon Relational Database Service (Amazon RDS) and Amazon Aurora flowing into Redshift using zero-ETL integrations to SageMaker Lakehouse Federated Catalog (Bring your own Amazon Redshift into SageMaker Lakehouse). With this integration, you can now seamlessly onboard the changed data from OLTP systems to a unified lakehouse and expose the same to analytical applications for consumptions using Apache Iceberg APIs from new SageMaker Unified Studio.

In this post, we show how to grant Amazon S3 permissions to IAM Identity Center users and groups using S3 Access Grants. We also test the integration using an IAM Identity Center federated user to unload data from Amazon Redshift to Amazon S3 and load data from Amazon S3 to Amazon Redshift.

Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables, the first cloud object store with built-in Apache Iceberg support. In this post, we guide you how to use various analytics services using the integration of SageMaker Lakehouse with S3 Tables.

In this blog post, we demonstrate how to use Amazon MWAA for centralized orchestration, while distributing data processing and machine learning tasks across different AWS accounts and Regions for optimal performance and compliance.