Building a Cloud-Native Data Platform
for Energy Trading Analytics
Designed and delivered a modular AWS data platform to ingest, standardise, and expose market and fundamentals data,
enabling analytics and model development for power trading.
enabling analytics and model development for power trading.
The Client
A leading global commodities trading group active across energy, metals, and bulk commodity markets. The organisation operates large-scale international trading activities spanning physical supply chains and derivatives markets, managing complex commercial operations across multiple geographies.
Within its energy division, the firm runs sophisticated power trading operations supported by quantitative analytics, market intelligence, and structured risk management capabilities. Its trading desks integrate market pricing, asset data, and fundamental datasets to support decision-making in highly dynamic commodity markets.
Within its energy division, the firm runs sophisticated power trading operations supported by quantitative analytics, market intelligence, and structured risk management capabilities. Its trading desks integrate market pricing, asset data, and fundamental datasets to support decision-making in highly dynamic commodity markets.
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Annual global commodities trading revenue
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Employees supporting global trading and logistics operations
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Active trading and supply chain presence worldwide
The Challenge
The energy trading division relied on multiple external market data providers delivering pricing, asset, and fundamental datasets with different structures, update frequencies, and integration mechanisms. Data ingestion processes were fragmented and not industrialised, limiting scalability and increasing operational dependency on manual handling and ad-hoc workflows.
The trading and analytics teams required timely access to reliable, standardised datasets to support quantitative modelling, pricing analysis, and risk assessment. However, inconsistencies in data formats, varying ingestion patterns (batch, event-driven), and the absence of a unified lifecycle model made it difficult to efficiently prepare and expose data for analytics consumption.
The organisation needed to design and implement a scalable cloud-native Data Platform MVP capable of industrialising ingestion, standardisation, storage, and governed data access—while remaining modular, extensible, and aligned with future enterprise architecture evolution.
The trading and analytics teams required timely access to reliable, standardised datasets to support quantitative modelling, pricing analysis, and risk assessment. However, inconsistencies in data formats, varying ingestion patterns (batch, event-driven), and the absence of a unified lifecycle model made it difficult to efficiently prepare and expose data for analytics consumption.
The organisation needed to design and implement a scalable cloud-native Data Platform MVP capable of industrialising ingestion, standardisation, storage, and governed data access—while remaining modular, extensible, and aligned with future enterprise architecture evolution.
Our Solution
Implemented a cloud-native Data Platform MVP on AWS to industrialise data ingestion, standardisation, storage, and governed access for energy trading analytics.
The architecture implemented a modular, event-driven design leveraging Amazon S3 for scalable object storage (RAW and SAFE layers), AWS Lambda and SQS for distributed ingestion orchestration, EMR with Spark for large-scale data standardisation and transformation, and Glue Data Catalog and Athena to enable SQL-based access for analytics and quantitative use cases.
The platform supported batch and event-driven ingestion patterns, enabling integration of high-frequency external market data sources while maintaining traceability, partitioned storage optimisation, and extensibility for future data products and trading use cases.
The architecture implemented a modular, event-driven design leveraging Amazon S3 for scalable object storage (RAW and SAFE layers), AWS Lambda and SQS for distributed ingestion orchestration, EMR with Spark for large-scale data standardisation and transformation, and Glue Data Catalog and Athena to enable SQL-based access for analytics and quantitative use cases.
The platform supported batch and event-driven ingestion patterns, enabling integration of high-frequency external market data sources while maintaining traceability, partitioned storage optimisation, and extensibility for future data products and trading use cases.
Event-Driven Ingestion
Implemented a scalable ingestion framework supporting batch and event-driven patterns. A dynamic scheduler, SQS queues, and Lambda-based ingestion pools enabled controlled, concurrent integration of thousands of external datasets while maintaining reliability and traceability.
Industrialised Data Lifecycle
Established a structured RAW → SAFE data lifecycle on Amazon S3. Data was ingested in its original form, then standardised and optimised for analytics consumption, ensuring controlled processing, partition management, and historical traceability.
Scalable Data Processing
Delivered a Spark-based standardisation framework on EMR to consolidate, enrich, and denormalise datasets at scale. The metadata-driven design accelerated development and enabled efficient transformation of high-volume market and fundamentals data.
Governed SQL Data Access
Exposed datasets via AWS Glue Data Catalog and Athena, enabling secure SQL-based access for analysts, quants, and data engineers. The architecture supported self-service analytics while maintaining metadata consistency and controlled schema management.
Resilient Orchestration
Implemented orchestration controls using DynamoDB, SQS, and CloudWatch to manage execution state, error handling, reconciliation, and monitoring. This ensured reliable ingestion cycles and controlled recovery from partial failures.
Modular Cloud Architecture
Built a fully cloud-native AWS architecture using serverless and managed services. The modular design supports future data sources, new trading use cases, and incremental evolution toward a production-grade enterprise data platform.
The Value
The platform established a scalable and industrialised foundation for ingesting, standardising, and governing multiple external power market datasets, including forward curves, time series pricing data, asset-level information, energy fundamentals, and weather data. By implementing structured ingestion patterns and event-driven orchestration, the organisation enabled controlled near real-time integration of high-frequency market intelligence into a unified analytics-ready environment.
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Weeks to deliver MVP
From design to operational market data ingestion and delivery in just 12 weeks.
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Market Curves
Industrialised ingestion and standardisation of 15,000+ power market curves via automated 10-minute cycles.
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Marke Data per Day
Ingestion of high-frequency forward curves and time series datasets, processing more than 3 GB of curve data daily.
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Real-Time Market Data Integration
Enabled ingestion and processing of forward curves, pricing time series, fundamentals, and weather datasets into governed storage layers.
Empowered Data Science & Trading Analytics
Provided the Data Science team with direct SQL-based access to standardised, analytics-ready datasets, accelerating historical analysis, model development, and trading signal generation.
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Industrialised Data Onboarding
Established a scalable framework to onboard and standardise new market data sources efficiently, reducing time-to-value for new trading analytics use cases.
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