Cloud Modernization & Data Platforms
We migrate legacy analytics estates to modern cloud data platforms — built for the latency, scale, and cost profile energy markets demand. That includes moving off dated stacks (HDInsight, R Server) onto architectures the team can actually maintain.
Book a Consultation →Typical engagement deliverables
- Cloud migration assessment and plan
- Platform build (lakehouse / warehouse)
- Legacy workload migration and decommissioning
- Cost optimization and FinOps guardrails
Technology & partner ecosystem
Cloud modernization is not a slide we drew for someone else.
We built our first-generation analytics platform on Azure HDInsight — Spark and Hive for compute, Presto for interactive query, R Server for data science, and batch ingestion feeding it all. It worked. And, like every estate of its era, it grew expensive to run and slow to evolve.
So we modernized it the same way we now modernize our clients’: onto a cloud lakehouse, with orchestrated pipelines in place of hand-maintained jobs, tested transformations, and data science on a platform the team could actually operate. We know exactly where these migrations get stuck — cluster sprawl, brittle pipelines, the “who still understands this job” problem — because we hit every one of them ourselves before we ever ran one for a client.
The estate we moved off
- Azure HDInsight — Spark & Hive compute
- Presto interactive query gateway
- R Server / RStudio for data science
- Batch ingestion, manually maintained
- Hand-managed clusters & cron scheduling
Where we run today
- Cloud lakehouse — Databricks & Snowflake
- dbt-modeled, tested transformations
- Airflow-orchestrated pipelines
- Python & R ML with managed retraining
- FinOps guardrails on elastic compute
Where cloud modernization & data platforms shows up in energy.
How a cloud modernization & data platforms engagement runs.
- 01
Assess
We start by understanding the current state — the data, the systems, and the real problem underneath the stated one.
- 02
Roadmap
We prioritize the work that will actually move the business, sequence it, and build the case for funding it.
- 03
Build
We deliver the pipelines, models, and reporting — working alongside your team, not in a silo.
- 04
Run
We hand over a solution your team can operate and evolve, with the enablement to keep it running after we leave.
Perspective on cloud modernization & data platforms.
Why regulatory reporting is a data problem before it's a compliance one
Filing season turns into a fire drill when the data is gathered by hand every time. The fix isn't more reviewers — it's a reporting foundation that makes "prepare a filing" and "answer an audit request" the same fast, traceable operation.
Jul 22, 2026 · Read →Predicting outages starts with connecting OMS, AMI, and SCADA
Outage prediction gets pitched as a machine-learning problem. On most grids it's an integration problem first — the model is only as good as the OMS, AMI, and SCADA data you can actually bring together in time.
Jul 13, 2026 · Read →What breaks first when you move ISO market data to the cloud
The migration rarely fails on compute. It fails on the assumptions baked into how ISO feeds arrive, reconcile, and get trusted — here's what to check first.
Jun 24, 2026 · Read →