Cubegle
Data • AI • Cloud
Case Study

Cloud & DevOps Modernisation for Data Workloads

Legacy ETL jobs, manual deployments, and unmonitored workloads were slowing down the business. Cubegle modernised the DevOps foundations for data and analytics.

Overview

The client had grown over the years with multiple scripts, ETL tools, and BI platforms. Releases were manual, downtime was frequent, and incident response lacked visibility.

Problem

  • No consistent CI/CD pipeline for data & analytics.
  • Manual deployment steps prone to human error.
  • Limited monitoring and alerting for failures.
  • Infrastructure changes not versioned or reproducible.
DevOps Modernisation Diagram
Snapshot

Domain: Multi-system data platform
Scope: DevOps for ETL, data, and BI
Outcome: Automated, observable, stable workloads

Solution

Cubegle implemented a DevOps layer that treated data projects like modern software projects: versioned, tested, automated, and monitored.

Key Workstreams

  • CI/CD pipelines for ETL, data models, and BI code.
  • Infrastructure as Code (IaC) for core components.
  • Logging and monitoring setup for data jobs.
  • Automated tests and validation checks in the pipeline.

Architecture (High Level)

Git-driven workflows deployed data pipelines, schemas, and BI assets using CI/CD pipelines integrated with the cloud environment.

Tech Stack

Git → CI/CD platform (GitHub Actions / Azure DevOps / Jenkins)
→ IaC (Terraform) → Cloud (Azure / AWS)
→ Logging & Monitoring

Impact

  • Fewer deployment incidents and faster rollback in case of issues.
  • Improved collaboration between data, engineering, and operations teams.
  • Higher reliability and transparency for stakeholders.
  • Clear path for further scaling without operational chaos.
Where This Applies

Ideal for organisations where data projects have grown organically and now require a disciplined DevOps layer around them.