AWS Case Study: QSR Adopts Amazon RedShift for Cost-Effective Analysis

AWS Case Study: QSR Adopts Amazon RedShift for Cost-Effective Data Analysis

We recently had the opportunity to work with a popular quick serve restaurant (QSR) who reached out asking if Flux7 could help speed its developer outcomes for faster time to market. For this global enterprise, the 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. Today we’d like to share the story of this AWS case study project.

We began the first two phases of the project by setting up AWS Service Catalog, Amazon’s service that allows organizations to create and manage catalogs of IT services that are approved for use on AWS. We set up AWS Service Catalog with eight products of two kinds: the first was an iis-stack which consisted of a Windows and 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 pre-defined controls, and standard tags, within minutes with minimal human effort.

Next, we set out to deploy and scale resources based on Amazon Redshift, AWS's fast, simple, cost-effective data warehouse service. To do so, the teams first needed to launch the Amazon Redshift cluster. The AWS experts at Flux7 did so by creating 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 Redshift tagging Lambda function 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. 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 to create RedShift clusters on-demand in a self-serve format.

Following setup, Flux7 conducted thorough knowledge transfer to this leading quick serve purveyor, 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.

As seen in this project, faster time to market is achieved through self-serve IT which allows developers to access and self-provision, growing their productivity as they they no longer need to wait in queue for system resources to be spun up for them. Indeed, this global enterprise took a process that formerly would have taken days, and reduced it to five minutes, growing the team’s efficiency and agility.

Moreover, by effectively integrating this S&P 500 firm’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. For additional reading on speeding time to market and reducing costs, please read our AWS Case Studies:

Sign Me Up!

March 13, 2018 / Retail, Data, AWS Service Catalog

About the Author

Flux7 Labs
Find me on:

Join Us

Join thousands of technology enthusiasts, subscribe and get expert perspective in your inbox.

Connect With Us

Recent Posts