Oil & Gas · Data Engineering
Modernizing Legacy ETRM Data with Azure
An oil and gas trading business had over a decade of trade and position history locked inside an ETRM with limited reporting flexibility, making historical analysis slow and dependent on IT.
The Challenge
Analysts needed to run historical scenario and performance analysis across years of trade data, but the ETRM's native reporting tools were built for operational reporting, not ad-hoc analytics, and every new report request required IT involvement.
Existing Environment
An enterprise ETRM as the system of record, with no existing data warehouse or analytics layer, and reporting requests handled through a backlog managed by the ETRM support team.
Root Cause
The ETRM's data model was optimized for transactional integrity, not analytical querying, and there was no separate platform where analysts could explore data independently without risking production performance.
Solution
We designed and built a cloud data platform on Azure that extracted trade, position and market data from the ETRM on a scheduled basis, modeled it for analytical use in Azure Data Lake and Databricks, and exposed it through Power BI - giving analysts self-service access without touching the production ETRM.
Data modernization architecture
Implementation
- Mapped the ETRM data model and identified the entities needed for analytics
- Built scheduled extraction pipelines using Azure Data Factory
- Modeled historical data in Delta Lake for consistent, versioned analytics
- Built a semantic layer and Power BI datasets for self-service reporting
- Trained the analytics team on the new self-service workflow
Result
- Analysts could build new historical reports without IT involvement
- Report turnaround time dropped from weeks to hours for most requests
- The ETRM's production performance was protected from ad-hoc analytical queries
Technologies
Lessons Learned
- Separating analytical workloads from the transactional ETRM is often the single highest-leverage data engineering investment a trading business can make.
- Self-service reporting adoption depends as much on training and semantic-layer design as on the pipeline itself.
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