Category: Amazon Redshift

This post is co-written with Preshen Goobiah and Johan Olivier from Capitec. Apache Spark is a widely-used open source distributed processing system renowned for handling large-scale data workloads. It finds frequent application among Spark developers working with Amazon EMR, Amazon SageMaker, AWS Glue and custom Spark applications. Amazon Redshift offers seamless integration with Apache Spark, […]

Building a data platform involves various approaches, each with its unique blend of complexities and solutions. A modern data platform entails maintaining data across multiple layers, targeting diverse platform capabilities like high performance, ease of development, cost-effectiveness, and DataOps features such as CI/CD, lineage, and unit testing. In this post, we delve into a case […]

This post was co-written with Rajiv Arora, Director of Data Science Platform at Gilead Life Sciences. Gilead Sciences, Inc. is a biopharmaceutical company committed to advancing innovative medicines to prevent and treat life-threatening diseases, including HIV, viral hepatitis, inflammation, and cancer. A leader in virology, Gilead historically relied on these drugs for growth but now […]

Amazon Redshift ML allows data analysts, developers, and data scientists to train machine learning (ML) models using SQL. In previous posts, we demonstrated how you can use the automatic model training capability of Redshift ML to train classification and regression models. Redshift ML allows you to create a model using SQL and specify your algorithm, […]

Like virtually all customers, you want to spend as little as possible while getting the best possible performance. This means you need to pay attention to price-performance. With Amazon Redshift, you can have your cake and eat it too! Amazon Redshift delivers up to 4.9 times lower cost per user and up to 7.9 times […]

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Data analysts and database developers want to use this data to train machine learning (ML) models, which can then be used to generate insights on new data for use cases such as forecasting […]

In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. We discuss various design patterns and architectures for extracting and cataloging valuable insights from unstructured data using AWS. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data.

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud, providing up to five times better price-performance than any other cloud data warehouse, with performance innovation out of the box at no additional cost to you. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to […]

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. Today, tens of thousands of AWS customers—from Fortune 500 companies, startups, and everything in between—use Amazon Redshift to run mission-critical business intelligence (BI) dashboards, […]

In this post, we show how to migrate a data warehouse from Microsoft Azure Synapse to Redshift Serverless using AWS Schema Conversion Tool (AWS SCT) and AWS SCT data extraction agents. AWS SCT makes heterogeneous database migrations predictable by automatically converting the source database code and storage objects to a format compatible with the target database.