Data Engineering
Pipelines, warehouses and infrastructure that make your data usable. Batch and streaming, warehouse and lakehouse, with the quality and governance that analytics and AI depend on.
Make your data usable
Most AI and analytics problems are really data problems. Without reliable pipelines, clean data and clear governance, models and dashboards are built on sand.
We build data platforms as production software: batch and streaming pipelines, a warehouse or lakehouse, and the quality checks and governance that make the data trustworthy.
- Batch and streaming pipelines built as production software.
- Warehouse or lakehouse suited to your workloads.
- Data quality and governance so results can be trusted.
What we build
-
Pipelines
Reliable batch and streaming pipelines that move and transform data with tests, observability and clear ownership.
-
Warehouse and lakehouse
A warehouse or lakehouse designed for your analytics and AI workloads, not a copy of someone else's reference architecture.
-
Quality and governance
Data quality checks, lineage and access governance so the numbers and models built on top can be trusted.
Frequently asked questions
Warehouse or lakehouse?
It depends on your workloads. We recommend based on your data, query patterns and AI plans rather than defaulting to one architecture.
How do you keep data trustworthy?
With automated quality checks, lineage and observability, so issues are caught in the pipeline rather than discovered in a report.
Is this a foundation for AI too?
Yes. Good data engineering is what makes machine learning and generative AI reliable, so we build with those workloads in mind.
Build AI and analytics on solid foundations
Tell us where your data hurts. We will scope the pipelines, platform and governance to make it usable.