Category: HAQM Redshift

This blog post demonstrates how to combine some of the OHDSI projects (Atlas, Achilles, WebAPI, and the OMOP Common Data Model) with AWS technologies. By doing so, you can quickly and inexpensively implement a health data science and informatics environment.

We have been busy adding new features and capabilities to HAQM Redshift, and we wanted to give you a glimpse of what we’ve been doing over the past year. In this article, we recap a few of our enhancements and provide a set of resources that you can use to learn more and get the most out of your HAQM Redshift implementation.

Over the years, I have developed and created a number of data warehouses from scratch. Recently, I built a data warehouse for the iGaming industry single-handedly. To do it, I used the power and flexibility of HAQM Redshift and the wider AWS data management ecosystem. In this post, I explain how I was able to build a robust and scalable data warehouse without the large team of experts typically needed.

When migrating from a legacy data warehouse to HAQM Redshift, it is tempting to adopt a lift-and-shift approach, but this can result in performance and scale issues long term. This post guides you through the following best practices for ensuring optimal, consistent runtimes for your ETL processes.

A few months ago, we published a blog post about capturing data changes in an HAQM Aurora database and sending it to HAQM Athena and HAQM QuickSight for fast analysis and visualization. In this post, I want to demonstrate how easy it can be to take the data in Aurora and combine it with data in HAQM Redshift using HAQM Redshift Spectrum.

After loading new data into an HAQM Redshift cluster, statistics need to be re-computed to guarantee performant query plans. By learning which column statistics are actually being used by the customer’s workload and collecting statistics only on those columns, HAQM Redshift is able to significantly reduce the amount of time needed for table maintenance during data loading workflows.

This is a guest post by Rafi Ton, founder and CEO of NUVIAD. The ability to provide fresh, up-to-the-minute data to our customers and partners was always a main goal with our platform. We saw other solutions provide data that was a few hours old, but this was not good enough for us. We insisted on providing the freshest data possible. For us, that meant loading HAQM Redshift in frequent micro batches and allowing our customers to query HAQM Redshift directly to get results in near real time. The benefits were immediately evident. Our customers could see how their campaigns performed faster than with other solutions, and react sooner to the ever-changing media supply pricing and availability. They were very happy.

Data security is paramount in many industries. Organizations that shift their IT infrastructure to the cloud must ensure that their data is protected and that the attack surface is minimized. This post focuses on a method of securely loading a subset of data from one HAQM Redshift cluster to another HAQM Redshift cluster that is located in a different AWS account.

We’re excited to announce today an update to our HAQM Redshift connector with support for HAQM Redshift Spectrum to analyze data in external HAQM S3 tables. With this update, you can quickly and directly connect Tableau to data in HAQM Redshift and analyze it in conjunction with data in HAQM S3—all with drag-and-drop ease.

Managing database users though federation allows you to manage authentication and authorization procedures centrally. HAQM Redshift now supports database authentication with IAM, enabling user authentication though enterprise federation. In this post, I demonstrate how you can extend the federation to enable single sign-on (SSO) to the HAQM Redshift data warehouse.