AWS CASE STUDY : Domino’s Pizza

6 min readSep 21, 2020

This Article is based on one of the case study of Global Cloud Leader that is Amazon Web Services ( AWS ) Cloud . In this Article we see about the case study of Domino’s with AWS Cloud .

“ Customers are getting their pizza faster, hotter, and fresher because of the improvements we’ve put into place with Project 3TEN. The predictive ordering solution we developed by using AWS is a big part of that.”

Michael Gillespie
Chief Digital and Technology Officer, Domino’s Pizza Enterprises Limited

Splunk Use Case: Domino's Success Story | Edureka

Going further to discuss aws case study first we will understand the basic concept of cloud computing.

What is Cloud Computing?

The company that provides their resources over the internet and charges according to the pay-as-you-go model, this type of company are called as a service provider or Cloud Company. This practice is called cloud computing.

Cloud computing is usually classified on the basis of location, or on the service that the cloud is offering.

Based on a cloud location, we can classify cloud as:

Public,
Private,
Hybrid
Community Cloud

Based on a service that the cloud is offering, we classify as:

IaaS (Infrastructure-as-a-Service)
PaaS(Platform-as-a-Service)
SaaS(Software-as-a-Service)
or, Storage, Database, Information, Process, Application, Integration, Security, Management, Testing-as-a-service

Now here comes the case study of aws :

🍕 About Domino’s :

When it comes to the global pizza business, Domino’s Pizza Enterprises Limited (Domino’s) has a large slice of the pie. The company, which is the largest Domino’s franchise holder, represents the Domino’s brand in Australia, New Zealand, Belgium, France, the Netherlands, Japan, Germany, Luxembourg, and Denmark. Domino’s maintains a network of more than 2,600 stores globally and is based in Brisbane, Australia.

The Challenge

Dominos’ order management system supports the major part of its business. While the customers could place orders through two interfaces — web and mobile, Dominos team was struggling with the performance issues of the order management system.

This mostly happened because many users’ browse menus and offers, but did not place an order and majority were not even logged into their system as customers. This resulted in high browsing load which the existing on-premise server could not handle. To combat the performance and scalability issues, Dominos wanted to migrate to AWS.

The Solution

BlazeClan conducted an exhaustive study of the existing system and charted out a roadmap to migrate their order management system to AWS.

The team of certified SAs proposed the following solution to overcome this issue:

Discovery of the existing architecture, assessing cloud readiness and designing of the AWS environment. This also included assistance in preparing the order management system cloud ready.
Implementation of the AWS environment, setting up IAM user management and authorization authentication as per the underlying best practices. This also included migrating the order management system to AWS and setting up Cloud Front to provide a better browsing experience. Implementation of the auto-scaling groups with ELB to make the existing order management system highly scalable and elastic in nature.
Testing of the order management system to validate support for 10s of thousands of concurrent users.

🍕 Benefits of using AWS :

Using the AWS-based solution, Domino’s has given its stores a tool to help drive down pickup and delivery times for customers.
For example, in 2019, a Domino’s store in Australia averaged delivery times of under 5 minutes, from order to doorstep, across an entire week. “It’s exciting that nothing changes from the customer’s perspective, except that the post-order experience can be much quicker,” says Gillespie. “Customers are getting their pizza faster, hotter, and fresher because of the improvements we’ve put into place with Project 3TEN. The predictive ordering solution we developed by using AWS is a big part of that.”
Assists Domino’s stores in achieving goal of pizza delivery in 10 minutes or less
Deploys accurate, predictive ordering solution quickly and easily
Enables fast, easy deployment for franchisees

🍕 Services of AWS Cloud Used by Domino’s

BlazeClan availed a number of AWS services to execute this project successfully.

Amazon EC2 :

It was used for computing capacity management for their application deployment. It helped in reducing the time required to spin up new server instances to minutes, allowing them to quickly scale capacity, both up and down, as per their requirement.

Amazon S3 :

It was used to store and retrieve any amount of data from anywhere and everywhere.

Amazon S3 is designed for 99.999999999% (11 9’s) of durability, and stores data for millions of applications for companies all around the world.

Amazon RDS :

This service was used to set up, operate and scale relational database and was mainly used for deployments.

AWS NAT Gateway :

was used to allow instances in a private subnet to connect to the Internet or to other AWS services.

ELK stack :

It was utilised for log aggregation and analytics. It is a combination of Elasticsearch (a NoSQL database and search server), Logstash (a log shipping and parsing service), and Kibana (a web interface that connects users with the Elasticsearch database and enables visualisation and search options for system operation users). It helped in providing a centralised and searchable repository for all infrastructure logs, thereby providing a unique and holistic insight to the customer.

Amazon SageMaker :

It is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

AWS Glue :

It is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL.