Category: Amazon Redshift

With SageMaker Lakehouse, you can access tables stored in Amazon Redshift managed storage (RMS) through Iceberg APIs, using the Iceberg REST catalog backed by AWS Glue Data Catalog. This post describes how to integrate data on RMS tables through Apache Spark using SageMaker Unified Studio, Amazon EMR 7.5.0 and higher, and AWS Glue 5.0.

Amazon SageMaker Lakehouse now supports attribute-based access control (ABAC) with AWS Lake Formation, using AWS Identity and Access Management (IAM) principals and session tags to simplify data access, grant creation, and maintenance. In this post, we demonstrate how to get started with SageMaker Lakehouse with ABAC.

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 post, we walk you through the process of setting up ThoughtSpot integration with Amazon Redshift using IAM Identity Center authentication. The solution provides a secure, streamlined analytics environment that empowers your team to focus on what matters most: discovering and sharing valuable business insights.

In this post, we discuss how to create AWS endpoint services to improve data security with Private Connect for Salesforce Data Cloud.

In this post, we demonstrate how to get started with S3 Tables and Amazon Redshift Serverless for querying data in Iceberg tables. We show how to set up S3 Tables, load data, register them in the unified data lake catalog, set up basic access controls in SageMaker Lakehouse through AWS Lake Formation, and query the data using Amazon Redshift.

In this post, we demonstrate how Amazon Redshift Serverless AI-driven scaling and optimization impacts performance and cost across different optimization profiles.

In this post, we dive into the newly released feature of Amazon Redshift Data API support for SSO, Amazon Redshift RBAC for row-level security (RLS) and column-level security (CLS), and trusted identity propagation with AWS IAM Identity Center to let corporate identities connect to AWS services securely. We demonstrate how to integrate these services to create a data visualization application using Streamlit, providing secure, role-based access that simplifies user management while making sure that your organization can make data-driven decisions with enhanced security and ease.

This post will explore brief history of zero-ETL, its importance for customers, and introduce an exciting new feature: history mode for Amazon Aurora PostgreSQL-Compatible Edition, Amazon Aurora MySQL-Compatible Edition, Amazon Relational Database Service (Amazon RDS) for MySQL, and Amazon DynamoDB zero-ETL integration with Amazon Redshift.

In this post, we explore the new higher base capacity of 1024 RPUs in Redshift Serverless, which doubles the previous maximum of 512 RPUs. This enhancement empowers you to get high performance for your workload containing highly complex queries and write-intensive workloads, with concurrent data ingestion and transformation tasks that require high throughput and low latency with Redshift Serverless.