Data Architecture Modernization
The Challenge
The customer needed a modern, secure, and scalable data architecture to support business intelligence (BI) and machine learning (ML). Their existing solution lacked scalability, relied partly on on-premises infrastructure, and required manual updates to data pipelines. Additionally, there was no version control and continuous deployment, creating inefficiencies and risks in the development process.
The Approach
- Conducted a full evaluation of the current state, which was built on Azure Databricks, Data Factory, Azure Data Lake Storage, and on-premises SQL servers.
- Designed both high-level and detailed target architectures aligned with best practices.
- Implemented 18 scalable, fully private environments across multiple regions.
- Migrated the data warehousing solution from on-prem SQL servers to Azure Databricks.
- Adopted the Medallion architecture pattern to streamline ETL workflows.
- Introduced Azure DevOps for automated code deployment and integrated GitHub the version control.
The Results
- BI delivery time reduced from 3 days to just 1 hour.
- Central data team shifted from reactive to proactive work model, with responsibilities delegated to local teams for faster execution.
- Established a resilient, scalable, and secure data architecture, directly contributing to organizational growth and innovation.