Cloud and DevOps Modernisation for Data Workloads
Migrated legacy ETL and reporting stack to a modern CI/CD-driven architecture with improved reliability and lower cost.
Overview
Cubegle helped a client experiencing operational friction from legacy systems and manual processes modernise DevOps practices for data and analytics workloads — the kind of situation where data projects have grown organically and now need a disciplined DevOps layer around them.
The challenge
- — Multiple scripts, ETL tools and BI platforms accumulated over time
- — No standardized CI/CD for data initiatives
- — Manual deployment steps prone to human error
- — Infrastructure changes with no version control or reproducibility
The architecture
- 01
Version Control
Git-centric workflow for all data and analytics code
- 02
CI/CD Orchestration
GitHub Actions, Azure DevOps or Jenkins
- 03
Infrastructure as Code
Terraform-managed cloud infrastructure
- 04
Cloud Platform
Azure or AWS
- 05
Observability
Logging and monitoring layer
What we built
- — Automated pipelines for ETL, data models and BI assets
- — Infrastructure as Code for reproducible deployments
- — Comprehensive logging and monitoring for data jobs
- — Test validation integrated into the deployment pipeline
Outcomes
- Reduced deployment incidents with faster issue resolution
- Enhanced collaboration among data, engineering and operations teams
- Strengthened workload reliability and stakeholder transparency
- Established a scalability foundation for future growth
Let's talk about what your data should be doing for you
Tell us where you are today and where you're trying to get to. We'll respond with a clear, honest read on the path forward.