Case study 02 · Sustainability & manufacturing
Reusable Azure Databricks Platform for ESG Analytics
From architecture vision and executive approval to a production platform for sustainability, carbon and supply-chain analytics.
The challenge
The client provides applications that help large manufacturers improve safety, sustainability and productivity. Data arrived from a wide variety of systems, but reporting and analytics needed a central, repeatable foundation capable of supporting ESG performance, carbon management and supply-chain risk use cases.
The engagement
The work began with a comprehensive data-architecture vision presented to executive leadership. After approval, an initial proof of concept was built and evolved into a production solution.
What was delivered
- Lakehouse architecture vision and an MVP implementation in Azure Databricks.
- A metadata-driven ingestion framework to onboard new source systems using repeatable patterns.
- A template approach for the data team covering best practices, SLAs, monitoring and data contracts.
- Data-quality controls for schema, null and duplicate thresholds.
- SLA-based alerting to improve operational reliability.
- New Databricks workflows and ETL processes implemented with Python and PySpark.
Turning a POC into a platform
Reusable platform flow
The platform pattern
- Onboarding logic is driven by metadata so new sources can follow a standard implementation path.
- Quality checks are embedded in the delivery flow rather than added manually after data lands.
- SLAs, monitoring and data contracts create an operating model for the platform - not just a set of pipelines.
- Architecture standards allow the data team to scale delivery while keeping implementations consistent.
From bespoke to reusable
The client gained a repeatable data-platform approach rather than having to design every new integration independently.
From concept to production
The architecture moved from executive vision to POC and then to a fully operational production product.
Technology
- Azure
- Azure Databricks
- Azure Data Factory
- Python
- PySpark
Commercial takeaway
The differentiator was not a single ETL pipeline. It was the reusable platform pattern around ingestion, quality, contracts, monitoring and delivery standards - creating a foundation that a growing data team can operate consistently.
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