Category: Amazon Redshift

Glue Data Catalog views is a new feature of the AWS Glue Data Catalog that customers can use to create a common view schema and single metadata container that can hold view-definitions in different dialects that can be used across engines such as Amazon Redshift and Amazon Athena. In this blog post, we will show how you can define and query a Data Catalog view on top of open source table formats such as Iceberg across Athena and Amazon Redshift. We will also show you the configurations needed to restrict access to the underlying database and tables. To follow along, we have provided an AWS CloudFormation template.

Amazon Redshift ML is a feature of Amazon Redshift that enables you to build, train, and deploy machine learning (ML) models directly within the Redshift environment. Now, you can use pretrained publicly available large language models (LLMs) in Amazon SageMaker JumpStart as part of Redshift ML, allowing you to bring the power of LLMs to analytics. You can use pretrained publicly available LLMs from leading providers such as Meta, AI21 Labs, LightOn, Hugging Face, Amazon Alexa, and Cohere as part of your Redshift ML workflows. By integrating with LLMs, Redshift ML can support a wide variety of natural language processing (NLP) use cases on your analytical data, such as text summarization, sentiment analysis, named entity recognition, text generation, language translation, data standardization, data enrichment, and more. Through this feature, the power of generative artificial intelligence (AI) and LLMs is made available to you as simple SQL functions that you can apply on your datasets. The integration is designed to be simple to use and flexible to configure, allowing you to take advantage of the capabilities of advanced ML models within your Redshift data warehouse environment.

In this post, we cover how to enable trusted identity propagation with AWS IAM Identity Center, Amazon Redshift, and AWS Lake Formation residing on separate AWS accounts and set up cross-account sharing of an S3 data lake for enterprise identities using AWS Lake Formation to enable analytics using Amazon Redshift. Then we use Amazon QuickSight to build insights using Redshift tables as our data source.

Large-scale data warehouse migration to the cloud is a complex and challenging endeavor that many organizations undertake to modernize their data infrastructure, enhance data management capabilities, and unlock new business opportunities. As data volumes continue to grow exponentially, traditional data warehousing solutions may struggle to keep up with the increasing demands for scalability, performance, and […]

In today’s data-driven landscape, organizations are seeking ways to streamline their data management processes and unlock the full potential of their data assets, while controlling access and enforcing governance. That’s why we introduced Amazon DataZone. Amazon DataZone is a powerful data management service that empowers data engineers, data scientists, product managers, analysts, and business users […]

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it straightforward and cost-effective to analyze all your data using standard SQL and your existing extract, transform, and load (ETL); business intelligence (BI); and reporting tools. Tens of thousands of customers use Amazon Redshift to process exabytes of data per […]

This post is written in collaboration with Mackenzie Johnson and Phil Catterall from ActionIQ. ActionIQ is a leading composable customer data (CDP) platform designed for enterprise brands to grow faster and deliver meaningful experiences for their customers. ActionIQ taps directly into a brand’s data warehouse to build smart audiences, resolve customer identities, and design personalized […]

In this post, we show you how to get started with Apache XTable on AWS and how you can use it in a batch pipeline orchestrated with Amazon Managed Workflows for Apache Airflow (Amazon MWAA). To understand how XTable and similar solutions work, we start with a high-level background on metadata management in an OTF and then dive deeper into XTable and its usage.

EchoStar, a connectivity company providing television entertainment, wireless communications, and award-winning technology to residential and business customers throughout the US, deployed the first standalone, cloud-native Open RAN 5G network on AWS public cloud. This post provides an overview of real-time data analysis with Amazon Redshift and how EchoStar uses it to ingest hundreds of megabytes per second. As data sources and volumes grew across its network, EchoStar migrated from a single Redshift Serverless workgroup to a multi-warehouse architecture with live data sharing.

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing ETL (extract, transform, and load), business intelligence (BI), and reporting tools. Tens of thousands of customers use Amazon Redshift to process exabytes of data per […]