Data Engineering & Business Intelligence
Data platforms that hold up — from modelling to the report in the specialist team.
The problem
Data rarely sits where you need it. It comes from different systems, is structured differently and contradicts itself in the detail. If you draw reports from sources like that, you get numbers — but nobody can say whether they are right.
That becomes expensive at the latest when decisions rest on it, or when traceability is required.
What we do
We build the path from the source to the report — and in such a way that it stays traceable.
That starts with data modelling: which entities exist, how they relate, what history is needed? Then the loading: interfaces to source systems, load logic, error handling, restart points. And finally the reporting layer, from which specialist teams can work themselves.
Every step is built so that it can be checked — not as documentation added afterwards, but as part of the architecture.
How we proceed
- Current-state review — Source systems, data volume, existing reports, open pain points
- Data model — Business and technical model, history, alignment with the specialist team
- Loading — ETL routes, interfaces, error handling, batch control
- Reporting — Marts, reports, dashboards — where the specialist teams work
- Betrieb — Monitoring, quality assurance, further development
Technologies
- Databases: Oracle, DB2, Microsoft SQL Server, PostgreSQL, MySQL, Teradata
- Cloud: Microsoft Azure, Google BigQuery
- Analytics: SAS, Microsoft Power BI, SSAS/OLAP, MicroStrategy
- Integration: IBM DataStage, Control-M, Unix Shell, Python
- Modelling: CA ERwin, PowerDesigner, Toad
Typical results
- A data warehouse that specialist teams use together — instead of separate silos
- Migration of existing data onto a new platform, without data loss
- Reports that specialist teams can adjust themselves
- Evaluations that stand up to an audit
Do you have a project in mind? Book a free initial consultation — no obligation and no sales pressure.