Quick-Serve Restaurant Adopts Amazon RedShift for Cost-Effective Data Analysis

Case Study

Flux7 Speeds Developer Outcomes for Faster Time-to-Market


This quick-serve restaurant (QSR) is a household name, with thousands of corporate and franchise stores across the globe.  As a leading quick-serve purveyor, this company is publicly traded on the NYSE and is part of the S&P 500. As a large enterprise, this corporation asked the Flux7 team if they could help speed developer outcomes for faster time to market.

This goal manifested itself in a project where Flux7 helped the QSR create one-click automated installations of various products, including Amazon Redshift, through AWS Service Catalog which helped make development more efficient and productive through automation that minimized process overhead. The last step of the process was to deploy and provision a Redshift warehouse service cluster as the data analytics team also wanted to use the AWS Service Catalog.


The first two phases of the project had Flux7 and its QSR customer setting up AWS Service Catalog with eight products of two kinds: the first was an IIS-stack which consisted of a Windows and an MsSQL instance. The second was a Tomcat stack which consisted of a Linux instance with RDS MySQL in the backend. In both cases, the application was pre-installed automatically: Tomcat on Linux and ISS in Windows. All products were tested and launched in the correct environments, with all of the customer’s predefined controls, and standard tags, within minutes with minimal human effort.

To deploy and scale resources based on Amazon Redshift, the teams first needed to launch the Amazon Redshift cluster. To do so, the AWS experts at Flux7 created individual AWS CloudFormation templates in JSON format to deploy resources to set up, operate and scale an Amazon Redshift Cluster. The cluster had several important considerations:

1.  The customer required that the mapping values were set to use a specific VPC.
2.  Product-specific parameters such as name, email, instance type, data classification, etc. were to be used.
3.  Last, the output would be the deployed product.

The customer relies heavily on tagging to control its environment. However, at the time of the project, CloudFormation didn’t support tagging on the Redshift cluster. Therefore, Using Lambda functions, the Flux7 team solved the issue, using the actual AWS API to tag the created resources with the appropriate naming convention.

The Redshift tagging Lambda function now implements tagging on Redshift clusters, parameter groups and subnet groups following the customer’s tagging nomenclature. It does so through a mapping that is used upon the creation of a Redshift product. Last, Flux7 consultants used roles and groups to create access to the customer’s portfolios, including the Redshift cluster. In this case, access was limited to Developers and the Redshift team only.

Following setup, Flux7 conducted thorough knowledge transfer, teaching the customer’s teams how to use the new Service Catalog products and Redshift cluster moving forward to maximize their effectiveness while ensuring long-term success.  Indeed, the company is already using the solution to minimize time to market. For example, what used to take the team 24 to 48 hours to provision, can now be spun up in fewer than ten minutes. Moreover, by effectively integrating the customer’s tagging policy and processes into the solution, they now also have billing tags associated with new assets, whenever a user spins up an instance, ensuring that they can do not only cross-charge between departments, but easily and effectively manage costs.

Quick Serve Restaurant Adopts Amazon RedShift for Cost-Effective Data Analysis

June 21, 2019