Data platforms: We turn data into intelligence.
We bring data from across your company into one place, checked and ready for dashboards, models and AI.
What we build
Ingestion
Connectors for databases, applications, event streams, files and APIs, with change data capture so source systems aren’t slowed down.
Streaming and batch
One set of pipeline logic for real-time events and scheduled loads, so both paths produce the same numbers.
Lakehouse or warehouse
Open table formats on storage you own, or the warehouse you already run.
Quality and lineage
Checks on every table, and lineage from each dashboard back through every pipeline.
Real-time analytics
Dashboards, metrics and alerts on fresh data, from one metrics layer every team shares.
Models and AI on top
Features, forecasts and language models built on the same governed data, with access rules that follow it.
Every number traced to its source.
How it works
Data arrives from your sources as streams and scheduled loads, lands in open tables in the lakehouse and passes quality checks before dashboards and models read it.
You see what every pipeline costs.
Why it holds up
Compute on demand
Storage and compute scale separately: heavy queries scale out and idle capacity shuts down.
Back-pressure and replay
Streams buffer bursts, and any pipeline can be replayed from raw data after a fix.
Schema contracts
Breaking changes are caught at the source, before they reach a dashboard. A batch that fails its checks is held until it’s fixed.
Observability
Freshness, volume and failures are tracked per pipeline, and the owner is paged when a table falls behind.
What we measure
- Data freshness
- p95 query latency
- Cost per terabyte processed
- Failed pipeline runs
- Tables covered by quality checks
- Time to add a new source
- Incidents found before users
Common questions
What does Stelcorp build for data platforms?
Stelcorp builds large-scale data platforms that bring data from across a company into one place, checked and ready for dashboards, models and AI: ingestion with change data capture, streaming and batch processing, a lakehouse or warehouse, quality checks and lineage, real-time analytics, and models on top.
Do we have to replace our data warehouse?
No. The platform uses open table formats on storage you own, or the warehouse you already run.
How does every team get the same numbers?
One set of pipeline logic serves real-time events and scheduled loads, so both paths produce the same numbers. Dashboards, metrics and alerts read from one metrics layer every team shares, every table has quality checks, and lineage traces each dashboard back through every pipeline.
How does the platform stay cost-efficient at volume?
Storage and compute scale separately: heavy queries scale out and idle capacity shuts down. You see what every pipeline costs, and cost per terabyte processed is one of the measures the project is judged by.
What happens when a source system changes?
Schema contracts catch breaking changes at the source, before they reach a dashboard. A batch that fails its checks is held until it’s fixed, any pipeline can be replayed from raw data after a fix, and the owner is paged when a table falls behind.
Start with a question.
One question your data can’t answer today. In three weeks we trace its sources, measure a baseline and build a working pipeline.
Tell us the question