Course · 7 chapters
Data Engineering with AI
Ship data pipelines with AI agents doing the first draft: dbt models, Dagster DAGs, quality checks, debugging, and warehouse cost. 7 chapters, about 1.9 hours, for data engineers.
What you'll be able to do
- Build dbt models with an AI agent
- Ship Dagster DAGs from a written spec
- Write quality checks that block bad loads
- Debug pipelines using Dagster run history
- Document lineage and metric definitions
- Cut warehouse cost with proven fixes
What's inside
- 1Data Engineering with AI: Start Here
Boot the Anchorwell data lab, a DuckDB warehouse with dbt and Dagster already running, and see how the path's six chapters connect before you touch your own stack.
- 2Agent-Assisted SQL & dbt Modeling
The dbt workflow where an AI agent drafts and you decide what actually ships to production.
- 3Pipelines by Spec: DAGs with Agents
Write the pipeline spec, drive an agent to build the DAG, and harden it with the retries and idempotency it won't add on its own.
- 4Data Quality as Code
Turn an agent's noisy first-pass checks into the few that actually stop a bad load.
- 5Debugging Broken Pipelines
Drive an agent through Dagster's run history and check results to find, verify, and fix what actually broke.
- 6Docs, Lineage & Definitions
Auto-generate column docs and lineage notes with an agent, review them like any other agent draft, then write down one metric definition everyone can point to.
- 7Warehouse Cost & Performance
Triage a warehouse's query history with an agent, then prove one fix with real before and after numbers.
Frequently asked questions
- What will I learn in the Data Engineering with AI course?
- You learn to put an AI agent to work across a full data engineering loop: drafting dbt models against a real schema, turning specs into Dagster DAGs, writing data quality checks that hold, debugging pipeline failures with run history, documenting lineage, and tuning warehouse cost. It runs 7 chapters in about 1.9 hours.
- Who is this Data Engineering with AI path for?
- It is built for data engineers and analytics engineers at a practitioner level who already work with SQL, dbt, or an orchestrator, and want to use an AI agent as a first draft partner rather than blind trust.
- Do I need dbt or Dagster experience before starting?
- Some SQL and data modeling background helps, since the path is practitioner level, but each chapter works from a shared DuckDB, dbt, and Dagster practice kit, so you build the specific dbt and Dagster skills as you go.
- How long is the path and is there a certificate?
- The path runs 7 chapters, including a Start Here orientation, for about 1.9 hours total. You earn a certificate on the AI Academy by Anthropos when you complete every chapter.
- Is the Data Engineering with AI course free?
- No, it is a paid path included with an AI Academy by Anthropos subscription, alongside the rest of the AI Engineering track.
Earn a certificate
Complete all chapters to receive your certificate of completion.