Problem Statement
The client is the Global Capability Center (GCC) of a leading global pharmaceutical company, managing enterprise data and analytics platforms that support operations across research, manufacturing, regulatory, and commercial functions.
The analytics ecosystem, built on Azure Databricks, manages 150,000+ datasets and 40,000+ data pipelines. As platform adoption expanded across teams and domains, the GCC began facing challenges related to cloud cost visibility, workload governance, and operational efficiency. Limited transparency into cloud spend, fragmented monitoring practices, and lack of unified data lineage made it difficult to manage costs, enforce governance, and scale the data ecosystem effectively.
Key Challenges
- Limited visibility into Azure Databricks spend across regions, domains, and projects
- Rapid proliferation of datasets and pipelines, increasing duplication and infrastructure costs
- Fragmented cost monitoring practices across teams and resource groups
- Lack of unified data lineage across Source Systems ? Azure Data Factory ? Databricks ? Power BI
- Inconsistent governance practices for dataset reuse, pipeline development, and cluster configurations
Solution Implemented
- Implemented a centralized FinOps and governance framework for Azure Databricks
- Developed FinOps dashboards to provide granular visibility into cloud spend across domains, projects, and workloads
- Enabled real-time monitoring of consumption patterns to support proactive cost management
- Implemented an end-to-end data lineage framework across Source Systems, Azure Data Factory, Databricks, and Power BI
- Standardized governance practices for dataset reuse, pipeline deployment, and cluster configuration
- Optimized clusters and autoscaling configurations to improve infrastructure utilization
- Conducted FinOps enablement workshops to promote cost-aware development and platform usage across teams
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