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Leading Fintech Company Increases Productivity by 40% with Enhanced Data Operations
ABOUT THE CUSTOMER

A premier provider of innovative financial technology services with a strong focus on leveraging cutting-edge technology to deliver superior financial products and services providing real-time financial insights, advanced risk management solutions, and seamless transaction processing capabilities.

THE CHALLENGE |

The customer experiences challenges in accelerating time to insight. It was essential to integrate DevOps strategies in their data operations for efficient and reliable processes. Manual deployments caused discrepancies between development, testing, and production environments. Manual testing resulted in slower release cycles and frequent production issues. Manual processes led to frequent errors and inconsistencies slowing down the deployment pipeline and agility of teams. Manual data handling processes were time-consuming and prone to errors, slowing down decision-making and impacting customer satisfaction.

THE SOLUTION |
  • CloudifyOps leveraged AWS DevOps services, Code Commit for source code management, Code Build to run the test cases, and CodePipeline to orchestrate the CICD pipelines.
  • Created repositories in Code Commit to host DAGs of MWAA, Glue ETL scripts, DDL/DMLs of Redshift. When DAGs are modified and pushed to Code Commit, Code Pipeline triggers to test the Python dependencies and DAG import errors and if tests run successfully the DAGs will be copied to S3 which is auto-synced with MWAA.
  • Push event to ETL repo triggers a pipeline to run the test cases and initiate a Lambda function that uploads ETL scripts to S3 and launches a Glue Job with the modified ETL code.
  • When DDL/DML scripts are modified, code commit triggers code build to build the container image and pushes it to Elastic Container Registry via Code Deploy.
  • EKS cluster pulls the container image, reads configuration in Ss3 bucket to determine execution steps and connects with Redshift cluster to execute the DDL/DML code.
BENEFITS DELIVERED |
  • Implementing AWS DevOps services reduced the deployment time by 80%.
  • 70% of the data assets are version- controlled. and Developers focused more on coding and less on deployment, leading to a 40% increase in productivity.
  • Automating the CD pipeline for the data warehouse resulted in 75% droppage of production incidents.
  • Quality gates in the pipelines ensured that code changes were thoroughly and consistently tested throughout the development lifecycle and increased the release cycle time by 30%.
TECHNOLOGY STACK |

AWS Code Commit, Code Pipeline, Code Build, Code Deploy, MWAA, Glue, S3, Lambda, Redshift, ECR

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