Category: HAQM Redshift

In this post, we will show you five HAQM Redshift architecture patterns that you can consider to optimize your HAQM Redshift data warehouse performance at scale using features such as HAQM Redshift Serverless, HAQM Redshift data sharing, HAQM Redshift Spectrum, zero-ETL integrations, and HAQM Redshift streaming ingestion.

In this blog post we shall cover how understanding real-time payout performance, identifying customer behavior patterns across regions, and optimizing internal operations required more than traditional business intelligence and analytics tools. And how since implementing HAQM Redshift and Zero-ETL, MassPay has seen 90% reduction in data availability latency, payments data available for analytics 1.5x faster, leading to 45% reduction in time-to-insight and 37% fewer support tickets related to transaction visibility and payment inquiries.

In this post, we’ll build on the first post in this series to show you how to set up an Apache Iceberg data lake catalog using HAQM S3 Tables and provide different levels of access control to your data. Through this example, you’ll set up fine-grained access controls for multiple users and see how this works using HAQM Redshift. We’ll also review an example with simultaneously using data that resides both in HAQM Redshift and HAQM S3 Tables, enabling a unified analytics experience.

In this post, we showcase how financial planners, advisors, or bankers can now ask questions in natural language. These prompts will receive precise data from the customer databases for accounts, investments, loans, and transactions. HAQM Bedrock Knowledge Bases automatically translates these natural language queries into optimized SQL statements, thereby accelerating time to insight, enabling faster discoveries and efficient decision-making.

In this post, we show how to grant HAQM 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 HAQM Redshift to HAQM S3 and load data from HAQM S3 to HAQM Redshift.

With SageMaker Lakehouse, you can access tables stored in HAQM 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, HAQM EMR 7.5.0 and higher, and AWS Glue 5.0.

HAQM 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.

HAQM SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with HAQM 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 HAQM 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.