Case Study
DevOps enabled Customer Insights for a Global M&E Conglomerate


Our client is a renowned global leader in the creation, production, distribution, licensing and marketing of all forms of media and entertainment content.

Their customer insights application provides insights into M&E data, such as viewership, downloads, streaming data, personalized playlists and entertainment apps usage. It also handles and services customer requests to develop new queries for analysis.

The application environment is frequently replicated for testing and verification; and is hosted across AWS accounts in the dev (non-production) and production environments.

The Challenge

Following are the key challenges that the customer faced, in handling the application environment.

Application Deployment

Frequent hand-offs between multiple teams – Data Science, Engineering and IT Operations caused discrepancies in the application performance, coupled with extended hours of deployment, i.e., 2 to 4 hours per day.

Service Requests Handling

The incoming high-volume operational service requests handled by the DevOps team, such as accounts creation, services, patching and production support, caused frequent delays. They also underwent frequent manual resolution processes, which caused further backlogs and operational delays. This saw an increase in the application Turn-Around Time (TAT).

Infrastructure and Environment Related Issues

The inherent software and hardware configuration processes were time-consuming, causing delays in the environment duplication process. Inadequate management of source code control and systems further hindered the scalability of the clustered compute resources.

Our Solution

To address the key challenges, Agilisium implemented a scalable and fully automated AWS cloud-based DevOps solution.

Key Tools Utilized

  • Jenkins, Ansible, CloudWatch, Datadog, Terraform and CloudTrail to build the DevOps framework.
  • Jenkins Continuous Integration to reduce the manual activities, by developing a robust deployment framework.
  • Jenkins for the creation of a unified project dashboard, to aid in status monitoring, build failures and code coverage.

Application Deployment

  • End to end Continuous Integration and Delivery Process using Jenkins for optimal deployment time.
  • 90% reduction in deployment time from 4 hours to 15 minutes.
  • 90% reduction in Time-to-Market.

Automated Resolution of Service Requests via DevOps

  • Process automation tools such as Terraform, Ansible, Jenkins and Airflow have reduced the customer requests resolution time.
  • 70-80% reduction in Turn Around Time.

Infrastructure and Environment Operations

  • Continuous Integration & Continuous Delivery process using Jenkins.
  • Terraform and Ansible were utilized for Infrastructure Automation and Configuration Management.
  • Enterprise ETL pipelines were implemented using Git, Jenkins, EC2 and Airflow.
  • Application alerts management and status monitoring were carried out, using AWS CloudWatch and Datadog.
Results and Benefits

The CI/CD enabled DevOps implementation process using Jenkins was done, for viewing and processing customer data.

Faster Time to Market (TTM)

  • 90% reduction in the application deployment time.
  • 60% reduction in manual errors processing.
  • 70-80% reduction in Turn Around Time for service requests handled by the DevOps team.

In summary, the operational overhead expenses have been largely reduced, enabling additional investments in infrastructure provisioning.

Cost Efficiency and Productivity Gains

The DevOps enabled solution favors a collaborative work culture between the Data Science, Engineering and IT Operations teams. The open source technology implementation has also reduced the software purchase and licensing costs.

In conclusion, the focus is now on core activities, such as seeking Insights via a Continuous Analytics Process.