Leading EU Energy Company

Qualysoft built a cloud-first data backbone that ingests live weather signals, reconciles them with forecasts, and exposes curated trading indicators via API; speeding decisions and sharpening cost models.

Key Results

Near real-time access to current vs. forecasted weather indicators for trading desks.

Unified data layer (staging → warehouse → semantic datasets) with governed lineage.

Faster data-source onboarding (from 3 APIs now, designed for Oracle & others next).

Simplified operations and maintenance through orchestrated, observable ETL/ELT.

Summary

We built a centralized, cloud-based data platform to extract, harmonize, and serve weather-driven trading indicators (temperature, solar radiation, wind, precipitation, etc.) via API and analytics layers.

Client

One of Europe’s Leading Energy Companies

Industry

Energy & Commodities Trading

Location

Europe

Size

Enterprise (multi-country operations)

Services

Data platform design & engineering, ETL/ELT orchestration, API enablement, data modeling, BI enablement, cloud migration, DevOps/FinOps

Technologies

Microsoft Azure, Databricks, Azure Data Factory (ADF), Power BI, REST APIs; star-schema data warehouse; monitoring & governance toolset

Allocated Team

Data architect, Databricks engineers, ADF/orchestration engineer, data modeler, BI developer, DevOps/FinOps engineer, project manager

Cooperation period / Project duration

Phase 1 (foundation & 3 API sources) with roadmap for staged expansions (Oracle and additional sources)

Our client needed a single, reliable data backbone to compare live weather feeds with forecast models and translate them into trading-ready signals that influence petrol trading costs.

The platform had to:

Consolidate multiple external APIs (and future enterprise sources like Oracle).
Provide quick, secure access via API for downstream apps and quant models.
Ensure data quality, lineage, and governance for auditability.
Deliver decision-ready datasets in Power BI with consistent, business-friendly semantics.
Migrate from on-prem constraints to a scalable, cost-efficient Azure architecture.

Cloud Data Foundation (Azure + Databricks) – Implemented a scalable ELT framework for ingesting and transforming multi-source weather and operational data; leveraged Delta paradigms for reliability and performance.
Orchestrated Pipelines (ADF) – Built modular pipelines with parameterized datasets, retry policies, and dependency management for resilient operations and simplified maintenance.
Curated Warehouse (Star Schema) – Modeled fact/dimension structures (e.g., time, location, indicator type, provider) to standardize analytics and accelerate discovery.
API Enablement – Exposed curated indicators and aggregates through secure APIs for rapid consumption by trading tools and internal applications.
Power BI Layer – Published certified datasets and standardized KPIs, enabling consistent self-service analytics and executive reporting.
Migration & Operations – Executed the on-prem → Azure transition, established observability (logging, alerts, SLAs), and applied FinOps practices for cost visibility and optimization.
Roadmap Readiness – Designed connectors and patterns to onboard Oracle and additional sources without rework; included data quality checks and governance policies.

Operational efficiency: automated, observable pipelines reduce manual handling and speed time-to-insight.

Real-time awareness: trading teams access current vs. forecast indicators via API and Power BI, informing pricing and hedging decisions.

Data reliability & trust: governed lineage, quality checks, and certified datasets standardize analytics across stakeholders.

Scalable & cost-aware: cloud-native architecture grows with demand while maintaining cost control.

Future-proof platform: ready to incorporate new data sources (e.g., Oracle), push/clean data via APIs, and expand analytical use cases without disrupting operations.