Tuning your Amazon Redshift and Tableau Software Deployment for Better Performance

Overview | What you'll learn: 

Amazon Redshift and Tableau Software’s ability to connect directly provides business users the power and agility to analyze and gain insights from data sets running into the billions of rows. Understanding how to optimize each of these technologies as they work together can yield considerable performance gains and ultimately shorten deployment cycles.

This paper addresses issues relating to query patterns, data modeling, and workbook construction in an effort to achieve optimal responsiveness.

We've also pulled out the first several pages of the whitepaper for you to read. Download the PDF on the right to read the rest.


Amazon Redshift is a fully managed, petabyte-scale data warehouse service. It is optimized for data sets ranging from a few hundred gigabytes to over a petabyte and costs less than $1,000 per terabyte per year.

Tableau Software is a business intelligence solution that integrates data analysis and reports into a continuous visual analysis process, one that lets everyday business users quickly explore data and shift views on the fly to follow their train of thought. Tableau combines data exploration, visualization, reporting, and dashboarding into an application that is easy to learn and use. Anyone comfortable with Excel can create rich, interactive analyses and powerful dashboards in a drag and drop environment and share them securely across the enterprise. IT teams can manage data and metadata centrally, control permissions, and scale up to enterprise-wide deployments. With Tableau’s release of Tableau Online, all of the front-end visualizations can also be published to the cloud, where the visualizations can directly query Amazon Redshift data warehouses.

Tableau software with Amazon Redshift provides a powerful, attractive, and easy to manage warehousing and analysis solution.

Improving Performance with Amazon Redshift and Tableau

You will want to follow good design and query practices to provide the best user experience possible when analyzing large data sets using Tableau. Improving responsiveness involves making solid database design decisions, performing regular data warehouse maintenance and following good practices when constructing Tableau visualizations.

Monitoring Query Performance

Pinpointing poor query performance can often be cumbersome, but Tableau’s built-in performance recorder helps diagnose a slow or inconsistent report quickly. After successfully identifying poor query performance in a Tableau analysis of Amazon Redshift, you can often resolve the issue by reviewing your tables and query design, tuning your queries, and making adjustments to your workbook.

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