Data Modeling for ETRM Systems
Master the art of designing scalable, high-performance data models for Energy Trading & Risk Management systems. This course bridges **trading domain knowledge** and **data architecture** — from trade capture to market risk and settlements.
Program Snapshot
- • Data modeling for physical & financial trades
- • Trade capture, reference & market data design
- • Risk exposure & P&L data structures
- • Integration models for ORE
Overview
Energy and commodity trading systems rely on well-structured, normalized, and extensible data models to manage high-frequency trade, market, and risk data. This course provides practical modeling techniques — from conceptual (UML/ERD) to physical implementation in modern warehouses (Snowflake, Databricks, PostgreSQL).
Who Should Attend
ETRM developers, data architects, business analysts, and risk data engineers.
Key Takeaway
End-to-end energy trading data model with sample schema, normalization & denormalization examples.
Prerequisites
Basic knowledge of SQL, trading workflow, and relational database design.
Curriculum — Modules & Topics
Module 1 — Foundations of Data Modeling
- Data modeling principles (conceptual, logical, physical)
- Normalization, keys, and referential integrity
- Choosing the right model type for trading systems
Module 2 — ETRM Trade Data Domain
- Physical vs financial trade structures
- Trade header, legs, and deal components
- Instrument and contract modeling for Oil, Gas, Power, FX, Metals
Module 3 — Reference & Market Data Models
- Counterparty, portfolio, location, and price curve modeling
- Market data ingestion, versioning, and pricing table design
Module 4 — Risk & Exposure Modeling
- MTM, Delta, Gamma — structuring P&L and risk factor tables
- Trade valuation links to ORE / Gravitas ETRM / QuantLib results
- Time-series risk cube and position aggregation
Module 5 — Settlement & Accounting
- Invoice & cash flow schema design
- Integration with GL and reconciliation models
- Journal entry and exposure mapping
Module 6 — Reporting & Data Warehouse
- Designing fact/dimension models (Star, Snowflake)
- Trade fact, risk fact, and position dimensions
- Aggregations for dashboards (Power BI / Tableau)
Hands-on Labs & Case Studies
Lab 1 — Build Core Trade Schema
Create normalized trade tables for oil futures and swaps in PostgreSQL or Databricks SQL.
Lab 2 — Market Data Modeling
Design time-series price table and integrate with risk cube for VaR reporting.
Lab 3 — Risk Aggregation View
Build a consolidated exposure view using SQL window functions and joins.
Case Study — Gravitas ETRM-style Model
Model a simplified Gravitas ETRM trade & risk data flow with reference data joins and MTM tables.
Pricing & Delivery Options
Self-paced
Recorded modules, schema templates, and SQL labs.
Cohort (Instructor-led)
6-week cohort with live workshops and real ETRM datasets.
Enterprise
Private sessions, platform integration, and data architecture consulting.
Contact & Custom Requests
Want an enterprise quote, private cohort, or a customized syllabus? Tell us about team size, preferred delivery and target outcomes.