ADE 101: Foundations
This course introduces agentic data engineering in plain terms: how agentic systems differ from AI-assisted tooling, where automation earns leverage across the DataOps lifecycle, and how to spot strong candidates to automate first. You will build a first pipeline in a guided lab and leave with a clearer view of which skills compound as agents absorb routine work.
The tone is direct, examples stay grounded in real data engineering, and we name tradeoffs honestly — no hype, no hand-waving.
The leverage in agentic data engineering isn't in the models — it's in knowing which decisions to hand off and which to keep.
What's in this course
| # | Module | Time | What you'll learn |
|---|---|---|---|
| 1 | Where Do You Stand? (Maturity Assessment) | 5 min | Baseline your team's current automation maturity; the course recommends where to start based on your experience level |
| 2 | What Is Agentic Data Engineering? | 20 min | The distinction between AI-assisted and agentic; the automation spectrum; what ADE means for your team |
| 3 | Why Now — The Convergence Moment | 20 min | Why the 2024–2025 window was the inflection point; the cost curve shift; the maintenance burden quantified |
| 4 | How Agents Work | 25 min | From LLM calls to agentic systems; the harness; non-determinism; what can go wrong |
| 5 | Context, Tools, and Triggers | 25 min | The three design decisions that determine whether your agent does something useful or something destructive |
| 6 | Agents Across the DataOps Lifecycle | 20 min | What agents can do from bringing data in through running, maintaining, and modernizing pipelines—and where to start first |
| 7 | Your First Agentic Pipeline (Lab) | 45–60 min | Build a real agentic pipeline hands-on in a guided lab environment |
| 8 | Durable Skills for the Agentic Era | 15 min | Five skills that compound most; your role after agents handle the routine work |
Total estimated time: ~3 hours (self-paced; lab module is 45–60 min)
What you'll be able to do
- Compare AI-assisted and agentic data engineering and articulate how the distinction should shape what your team builds, buys, and staffs
- Use the CTT framework (Context, Tools, Triggers) to scope and design agents that stay reliable within clear boundaries
- Prioritize automation candidates on your team using the Automation Matrix (a prioritization framework you will use in Module 6)
- Build, run, and verify an agentic pipeline end-to-end in a guided lab environment
Prerequisites
No prior agent-building experience is required. Recommended background: about a year working with data pipelines and comfort with transformation and orchestration concepts (for example dbt, Apache Airflow, or equivalent tooling) — guidance, not a gate. If you've used an AI coding tool in the last six months, you're ready.
Module 8 is a hands-on lab in an Ascend trial workspace, using the Otto's Expeditions sample project. You'll work through six phases: sign up and connect a runtime (Setup), orient to the platform (Tour), explore the data (Explore), write a code standards rule (Context), build a new flow (Build), and automate it with a schedule and failure alert (Trigger). Plan 60–75 minutes (allow extra time if you're new to trial signup). The workflow and design patterns transfer to other vendors and stacks — you don't need Ascend to apply what you learn.
Most modules include a quiz — Module 1 includes a self-assessment instead (no scored pass/fail). Pass the knowledge checks on all seven content modules the certificate tracks — What Is ADE, Why Now, How Agents Work, Context, Tools, and Triggers, Agents Across the DataOps Lifecycle, Durable Skills, and Your First Pipeline — to unlock the ADE Foundations certificate at the bottom of this page. Quizzes are typically 2–3 questions. Retries are allowed — there's no penalty for trying again.
Take the 5-minute maturity assessment first — your results tell you where your team sits on the automation spectrum, and the course becomes more useful with that baseline in place.
Next: Where Do You Stand? →
Additional Reading
- ADE Systems Design (201) — Architecture and end-to-end system design for teams ready to build beyond the basics.
- ADE Production (301) — Running agentic systems reliably in production: observability, failure handling, and governance.