Building High-Performant Future-Proof Data Pipelines
Migrated a monolithic Oracle-based data platform to a modern, fully automated AWS architecture.
Überblick
A leading European bank needed to transition internal data workloads from Oracle to a modern, cloud-based architecture — one capable of handling various data loads while carefully managing sensitive and critical business data. DataMax led the complete design and development of the new architecture, established onboarding standards for new data sources, enabled thorough code and data quality testing, broke down the monolithic system into microservices, and automated the full release pipeline.
Herausforderung
The existing Oracle-based system was a single monolith: a data load triggered a chain of stored procedures running in strict linear order, with a 30-minute execution floor that couldn't be improved without major refactoring. The business logic inside those procedures had no test coverage, making changes risky. Data reached production without quality checks, causing inaccurate reports. Releasing changes required multiple manual steps and prolonged human intervention.
Vorgehen
We rebuilt the architecture on AWS, converting the entire stored-procedure codebase to PySpark and introducing automated CI/CD, linting, unit tests, integration tests, and data quality gates before anything reached production.
Lösung
Infrastructure: AWS RDS (PostgreSQL) for the database layer, PySpark for data processing, AWS Glue for scheduled job execution, AWS S3 for job artifacts, and ECS for the cloud migration service. Development: Python as the primary language, Poetry and Ruff for project management, pytest for unit and integration testing, and GitHub Actions for fully automated CI/CD across all environments.
Ergebnisse
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