Data engineering for energy and utilities
We build the data pipelines that turn sensor, grid and asset data into a trustworthy foundation for energy and utilities: real-time and batch ingestion, a governed lakehouse and forecasting inputs, delivered by one accountable Veratas team.
Energy and utilities run on data that arrives constantly and never stops
The data estate of an energy or utilities business is high volume, time sensitive and operationally critical.
Energy and utilities organisations generate data without pause. Smart meters, grid sensors, SCADA systems and field equipment produce a continuous stream of readings, while asset registers, outage logs and weather feeds add context. The volume is large, the time dimension is everything, and much of the value decays quickly: a reading that matters now is far less useful an hour late.
That mix forces two different data engineering patterns to coexist. Some data has to be ingested in real time to support grid operations and rapid response, while other data is better handled in scheduled batches for billing, settlement and longer term analysis. A pipeline that only does one well leaves the business either blind to current conditions or unable to reconcile the historical record.
On top of this sits forecasting. Demand prediction, renewable generation estimates and asset maintenance planning all depend on clean, well structured inputs drawn from sensors, assets and external feeds. If the underlying data is late, incomplete or inconsistent, the forecast inherits those flaws. Veratas builds the engineering layer that makes this data trustworthy, delivered by one accountable team accredited to ISO 27001, ISO 9001 and CMMI Level 3.
What strong data engineering changes for energy and utilities
The areas where well built pipelines turn raw operational data into something the business can rely on.
Real-time sensor ingestion
We build streaming pipelines that ingest smart meter, grid sensor and SCADA data as it arrives, landing it in the lakehouse with low latency so grid operations and response teams work from current conditions.
Batch pipelines for settlement
We use Azure Data Factory to orchestrate scheduled ELT pipelines for billing, settlement and historical analysis, so the reconciled record is complete, consistent and ready when finance and operations need it.
Asset and network data
We integrate asset registers, network topology and maintenance history into the lakehouse, giving a single structured view of equipment that supports condition monitoring and maintenance planning.
Reliable forecasting inputs
We prepare clean, time aligned datasets from sensors, assets and external feeds such as weather, so demand and generation forecasts are built on inputs that are dependable rather than guessed.
A governed lakehouse on Fabric
We build the platform as a lakehouse on Microsoft Fabric and OneLake, with Microsoft Purview cataloguing and lineage, so high volume operational data is organised, discoverable and governed.
How Veratas delivers data engineering for energy and utilities
A clear route from scattered operational data to a governed, dependable data platform.
Map sources and flows
We catalogue sensor, SCADA, asset and external data sources, assess volume and latency needs, and design the lakehouse and pipeline architecture to suit them.
Build the ingestion layer
We implement streaming pipelines for time sensitive data and Azure Data Factory batch pipelines for the rest, landing both in a structured lakehouse on Microsoft Fabric.
Model and assure quality
We model the data into clean, analysis ready layers and embed data quality checks and validation, so errors are caught before they reach forecasts and reports.
Govern and operate
We apply Microsoft Purview for cataloguing and lineage and run the pipelines as a managed service, with monitoring that keeps data flowing reliably.
FAQ
Frequently asked questions
Quick answers to questions you may have. Can't find what you're looking for? Check out our full documentation.
Related industry solutions
Other ways Veratas applies Microsoft and data engineering to industry-specific challenges.
Data engineering for financial services
We build the data pipelines that financial services depend on for regulatory reporting, risk and customer data: governed ELT with provable quality, lineage and audit, delivered by one accountable Veratas team.
Microsoft Fabric for energy and utilities
Microsoft Fabric brings grid, asset and operational data into one lakehouse so energy and utility operators can monitor networks and meet regulatory demands.
Build a dependable data foundation for your network
Book a discovery call and we will review your sensor, grid and asset data, then set out a clear route to pipelines and a lakehouse you can rely on.