From Fragmented
Data to a
Single Source
of Truth.
Pattern Energy runs one of the most complex data environments in clean energy — metering systems, SCADA, Oracle ERP, weather forecasting, and financial reporting all converging into a single enterprise platform. The Zig built the integrations, led the migration, and engineered the infrastructure that holds it together at 99.9% uptime.
Clean energy.
Systems don’t agree.
Pattern Energy is one of North America's leading independent renewable energy companies, operating wind, solar, and natural gas facilities across the continent. At their scale, data is not a support function. It is a core operational asset.
Pattern Energy operates across multiple systems each with its own data structures, update cycles, and responsibilities. Metering systems, SCADA, meteorological feeds, ERP, CRM, and financial reporting all produced critical data but not in a way that stayed consistently aligned. At this scale, the challenge wasn’t access to data. It was ensuring that operational and business systems reflected the same reality.
Pattern had already begun building its HIVE Data Platform. The work ahead was to integrate systems at scale, support a critical ERP migration, and ensure everything operated reliably under SLA.
A Medallion
Architecture.
for systems that must agree.
Pattern Energy’s Databricks Lakehouse uses a three-layer medallion architecture to manage data from ingestion through to reporting.
Data from multiple systems is progressively standardised across each layer, resolving inconsistencies, aligning formats, and applying business rules.
This structure ensures that changes in upstream systems don’t break downstream reporting, and that operational and financial systems reflect the same underlying data.
The Zig contributed across data architecture, modelling, and DevOps, ensuring the platform could be deployed, tested, and maintained consistently at scale.
One system.
Built across five layers.
The Zig worked across Pattern Energy’s data platform from integration through to reporting, as a single system.
Each area was delivered in coordination with the others, ensuring that data remained consistent across systems, and that changes in one layer didn’t break another.
The result was not a collection of components, but a platform that operated reliably end-to-end.
"The Zig has provided consulting services to support multiple areas of our data platform. They led the migration from D365 to Oracle and helped us build robust integrations between Oracle and our data platform — engineered to meet our SLAs. Their contributions extended across data architecture, data modelling to support reporting requirements, and DevOps to build scalable deployment pipelines. The Zig also established thorough end-to-end implementation documentation, ensuring long-term maintainability and operational clarity across the team."
The Stack
Behind the Platform.
Systems don’t maintain themselves.They need to be understood.
Enterprise data platforms depend on the teams that maintain them. Without clear documentation, even well-designed systems degrade over time. Every integration, pipeline, and architectural decision was documented, so Pattern Energy’s team could operate and extend the platform independently after delivery. With 99.9% uptime across 35+ integrations, the system was required to perform consistently under real operating conditions. That level of reliability depended on disciplined execution at every layer, from integration design through to deployment. Long-term maintainability wasn’t treated as an afterthought. It was built into the system from the beginning.
Ready to build
something that
holds?
Tell us what your data infrastructure looks like and where it's holding you back. We will be direct about what it takes to fix it — and how fast.