Big Data
Lakehouse and streaming data platforms that handle petabyte scale with governance and lineage from day one.
Data at scale is only an asset when it is governed and trustworthy. We build lakehouse and streaming platforms that unify batch and real-time data, with lineage, quality, and access control designed in — so teams can move fast without losing track of where the numbers came from.
A few of these usually point here.
- 01Data is siloed and pipelines are brittle.
- 02Nobody can trace where a number actually came from.
- 03Batch and streaming live in separate, costly stacks.
What we do
Lakehouse platforms
Unified data engineering, warehousing, and BI on Databricks or Microsoft Fabric.
Streaming & real-time
Event pipelines for real-time processing, tracking, and exception handling.
Data quality & lineage
Automated reconciliation and end-to-end lineage so data stays trustworthy.
Governance & access
Cataloging, access control, and policy so scale never means chaos.
- One governed platform for batch and real-time data
- Lineage and quality you can point to when the numbers are questioned
- Scale without losing control of access or cost
We design the target lakehouse and governance model first, migrate by domain, and reconcile outputs against the source of truth before cut-over.
Concrete artifacts you own.
- A governed lakehouse unifying batch and real-time data.
- Streaming pipelines for real-time processing.
- End-to-end lineage and automated data-quality checks.
- Cataloging and access control built in.
Good questions.
Databricks or Microsoft Fabric?
Whichever fits your ecosystem and workloads. We recommend on fit — Fabric for Microsoft-centric estates, Databricks for heavy engineering and ML.
How do you keep scale from becoming chaos?
Governance from day one — lineage, quality, catalog, and access control — so growth never costs you control.