ETRM Data Engineering with Azure

ETRM platforms accumulate years of valuable trade, position and P&L history, but native ETRM reporting tools are usually built for operational reporting, not flexible historical analytics. A cloud data platform - most commonly built on Azure in enterprise ETRM environments - closes that gap without touching production.

Why Azure specifically

Azure isn't the only viable cloud platform for this pattern, but it's the most common fit we see in enterprise ETRM environments, largely because many trading businesses already run other Microsoft infrastructure (SQL Server, Active Directory, Power BI) and Azure integrates naturally with that existing footprint.

The core components

• Azure Data Factory - orchestrates scheduled extraction from the ETRM and other source systems
• Azure Data Lake - stores raw, extracted data before transformation
• Databricks - transforms and models the data, commonly using Delta Lake for versioned, reliable datasets
• Azure SQL / Synapse - serves modeled data for reporting and analytics
• Power BI - the most common self-service reporting layer on top

Architecture pattern

Getting extraction right without disrupting production

The most important design decision is how data gets out of the ETRM without adding load or risk to the production system. Scheduled, incremental extraction (pulling only what's changed since the last run) during low-activity windows is the standard approach, rather than full daily extracts or direct live querying against the production database.

Modeling historical data for analytics

Once data lands in the lake, it needs to be modeled specifically for analytical use - which usually looks different from the ETRM's own transactional schema. Delta Lake's versioning is particularly valuable here: it lets analysts query the data as it existed at a specific point in time, which matters for historical scenario analysis and audit purposes.

Where AI and advanced analytics fit in

Once trade, position and market data are consolidated in a well-modeled cloud data platform, it becomes practical to layer more advanced analytics on top - forecasting, anomaly detection, scenario modeling - none of which is realistic to build directly against a live transactional ETRM database.