About the client
Headquartered in Houston, Texas, the customer is one of the leading suppliers of fuels, lubricants, and petrochemicals in the United States. With a vast nationwide network of branded stations, the organization has been delivering reliable energy solutions for over a century.
Beyond fuels, the client has diversified into specialty lubricants and industrial oils, in addition to being recognized for its sustainability and community-driven initiatives.
Client challenges
The customer’s data environment was increasingly fragmented, siloed, and difficult to govern. Multiple systems and datasets slowed down reporting cycles, reduced trust in data quality, and created performance bottlenecks. As the organization moved toward more AI-driven operations, its legacy architecture struggled to keep pace with the demands of scale, governance requirements, and analytics.
They partnered with our team to modernize their data ecosystem by deploying a unified, scalable platform powered by Microsoft Fabric. The engagement focused on governance, automation, and AI-readiness while enabling flexible yet secure access across business units.
Solution
We began with a comprehensive review of the client’s data environment through our discovery and assessment roadmap, mapping governance needs, cataloging requirements, and domain and subdomain structures, as well as security controls. Key data sources, volumes, workloads, and user access demands were evaluated to ensure the platform design could support enterprise-scale requirements. Post assessment, we carried out the following:
- Data Migration: Transferred one year of marketing data from Azure Synapse into Fabric’s OneLake, consolidating diverse sources into a single source of truth. We built an enterprise-ready architecture that extends the value of Azure investments—using IT-managed domains and secure shortcuts to deliver governed, flexible access for business units.
- Governance and Compliance: Established endorsement workflows (Promote, Certify, Master Data), lineage tracking, Purview integration, and governance dashboards.
- AI and Data Science Enablement: Delivered Spark-optimized ML experimentation environments, experiment tracking, Git-based Dev/Test/Prod workflows, and best-practice frameworks.
- Automation and Scalability: Built Azure DevOps pipelines for automated ELT, workspace provisioning, and governance enforcement, creating a repeatable model for future business units.
Benefits
- Future AI-driven analytics and predictive insights.
- Improved data security, transparency, and compliance with regulations.
- Self-service access to trusted, high-quality data across business units.
- High availability and performance of data-driven insights.
- Enriched governance capabilities with data cataloging, security labels, and end-to-end lineage.