90%
Infrastructure change review time cut
80%
DevOps time saved
100%
Automation deploy
The challenge
Breaking down barriers to modernize legacy data systems
A major public transport provider aimed to eliminate reliance on a third-party NetBI platform for transport and GIS data management. An initial proof of concept addressed basic data needs but exposed slow manual deployments, inadequate monitoring, delayed vulnerability detection, scalability issues, and other inefficiencies. These gaps underscored the need for a modern DevOps framework.
Key challenges
Limited monitoring caused extended system downtimes
Post-deployment security vulnerabilities created risks
Manual deployment processes blocked rapid delivery
Infrastructure management lacked automation
Scalability constraints limited growth
The solution
DevOps transformation framework with AWS integration
Unified infrastructure hub
ETL orchestration with AWS managed Apache Airflow
AWS ECS with Fargate for serverless containers
ECR integration for container management
CloudFormation for infrastructure as code
CI/CD automation
GitHub Actions for deployment automation
CloudWatch and Grafana monitoring
Automated vulnerability scanning
OIDC-based security integration
Real-time code quality checks
Implementation approach
1
Discovery and design
Evaluated existing NetBI dependencies
Designed AWS architecture
Identified security vulnerabilities
2
Development and testing
Implemented containerization
Created monitoring systems
Developed CI/CD pipelines
3
Enterprise rollout
Deployed across multiple environments
Enabled team automation
Optimized workflows
The impact
Measurable enterprise-wide results with significant cost savings
Operational excellence
90%
Time reduction
Self-service capabilities reducing dependency on external tools
Automated deployments
Infrastructure as code
Security enhancement
100%
Protection level
Early vulnerability detection
Automated security scanning
Real-time monitoring
DevOps efficiency
80%
Time savings
Streamlined infrastructure change reviews
Automated deployment processes
Reduced manual intervention
Looking ahead
Expanded automation
Rolling out additional automation capabilities
Enhanced analytics
Incorporating advanced monitoring features
Platform evolution
Further optimizing data management processes
