Build the data foundation that AI applications require
AI models are only as good as the data they run on. This intensive day teaches you the patterns behind robust, AI-ready data pipelines: from source integration and transformation to data quality and monitoring.
What you'll learn
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Pipeline architecture
Batch vs. streaming, medallion architecture, source integration patterns — when to choose what.
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Transformation with dbt
Writing modular SQL, testing data quality, automating documentation. From staging to mart.
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Data quality & testing
Freshness checks, schema tests, anomaly detection — built into the pipeline.
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Monitoring & observability
How to know your pipeline is working — and how to know when it isn't.
Programme
A day in detail — no surprises.
| # | Time | Module | Content |
|---|---|---|---|
| 1 | 09:00–09:30 | Start & stack check | Review current architecture, tooling check, day overview. |
| 2 | 09:30–10:45 | Pipeline architecture | Medallion layers, batch/streaming trade-off, connector patterns. Draw architecture for your own use case. |
| — | 10:45–11:00 | Break | |
| 3 | 11:00–12:30 | dbt in practice | Build staging, intermediate and mart models. Write tests and generate documentation. |
| — | 12:30–13:15 | Lunch | |
| 4 | 13:15–14:30 | Building in data quality | Great Expectations or dbt tests — freshness, not-null, referential integrity. Triage flow on failure. |
| — | 14:30–14:45 | Break | |
| 5 | 14:45–16:00 | Monitoring & alerting | Lineage, SLA checks, alerting patterns. Hands-on with a real dashboard. |
| 6 | 16:00–17:00 | Reviewing your own pipeline | Participants review each other's pipeline designs against the day's frameworks. Concrete improvements. |
Immediate results
- Own pipeline architecture drawn and reviewed
- Working dbt models including tests
- Data quality checklist for your stack
Long-term
- Fewer data incidents through built-in quality controls
- Faster onboarding of new team members
- Pipelines stakeholders can trust
Who it's for
- Data engineers
- Analytics engineers
- Software developers
- BI developers
About the trainer
Pascal Mertens
Co-founder & Data and Analytics Consultant
Co-founder of DNMP and economist (University of Amsterdam) with a sharp focus on data, BI and analytics. Pascal previously co-founded Vizieo, a data visualisation and analytics company. Today, at DNMP, he helps organisations turn data into actionable insights and better decisions.
LinkedInParticipant experiences
We stopped writing ad-hoc scripts and now build genuinely modular pipelines. dbt was a revelation.
I already knew medallion architecture in theory — after this day I understand when to switch approaches.
Data quality was always an afterthought. Now it's baked into every pipeline we build.
The peer review at the end was brutal but valuable. Discovered blind spots in my architecture I'd been carrying for months.
Our stakeholders now trust the data. That's no small step — that's a culture change.
Book an intake — we'll align the stack choices and use cases with your current environment.
Not sure about the fit?
Book an intake — we'll spend 30 minutes on your goals and give you honest advice.