Learn Agentic Data Engineering
The ADE learning path — from first principles to production. Built by the Ascend team.
By the end of this path, you'll have built agentic data pipelines, designed governance frameworks, and created a 90-day adoption roadmap for your organization. You'll understand the compound reliability math of multi-agent systems, the observability disciplines that detect reasoning drift before it becomes data quality failure, and the workflow redesign patterns that create leverage instead of marginal speedup.
This is a practitioner course for working data engineers. The tone is direct, the examples are grounded in real data engineering work, and we name the tradeoffs honestly.
The teams that master agentic data engineering don't just move faster — they operate at a scale that isn't reachable with traditional automation. That's what this path is built to teach.
The learning path
| Course | Duration | What you'll gain | |
|---|---|---|---|
| ADE Foundations (101) | ~3 hours | From curious to capable — what agentic data engineering is, how agents actually work, and how to design the context, tools, and triggers for a reliable agentic system | Start → |
| ADE Systems Design (201) | ~3 hours | From capable to confident — multi-agent architecture, context engineering at depth, governance frameworks, DataOps integration, and trust-and-verify patterns | Start → |
| ADE Production (301) | ~3 hours | From confident to operational — production deployment strategies, scaling from 20 to 200 pipelines, observability for reasoning drift, and a 90-day org adoption roadmap | Start → |
Total path: ~9 hours across 20 modules — self-paced, no gates, all content freely accessible.
Where does your team stand?
Take 5 minutes to benchmark your team's current automation maturity. The assessment covers four dimensions — technology, process, culture, and skills — and gives you a recommended starting point immediately.
What you'll be able to do after completing the full path
- Explain the difference between AI-assisted and agentic data engineering — and why the distinction drives completely different investment decisions
- Design agents that are reliable, appropriately scoped, and production-ready using the CTT framework (Context, Tools, Triggers)
- Build multi-agent systems with the right coordination patterns, state management, and partial failure defenses
- Detect reasoning drift before it becomes a data quality failure — and debug it when it does
- Create a 90-day adoption roadmap for your organization with concrete pilot selection, success metrics, and a business case
Earn your ADE Practitioner certification
Complete all three courses and you earn the ADE Practitioner certificate — the comprehensive credential that demonstrates you can design, build, operate, and scale agentic data engineering systems end-to-end.
| Certificate | Requirement |
|---|---|
| ADE Foundations | Complete all ADE 101 knowledge checks |
| ADE Systems Design | Complete all ADE 201 knowledge checks |
| ADE Production | Complete all ADE 301 knowledge checks |
| ADE Practitioner | All three certificates |
Each course has its own certificate. The Practitioner credential requires all three. All content is freely accessible — no paywalls, no gates.
About this learning path
This learning path is built by the Ascend team — the people behind Otto. The content reflects what we've learned working with data engineering teams at dozens of companies, from early pilots to org-wide production deployments.
Every framework, checklist, and exercise is something practitioners have used on real pipelines with real data. The citations point to peer-reviewed research, major analyst reports, and practitioner surveys — not vendor whitepapers.
Ready to build? Start the learning path, or book a focused working session with an Ascend engineer to apply what you learn to your actual pipeline portfolio.