The program
AI-Enabled Database Developer
Subject-matter first. We teach the job — designing, securing, deploying, and AI-enabling real database systems — and the credential follows. Every sub-course teaches beyond the exam objectives, because the goal is competence, not a certificate you can’t back up.
What makes it different
Four claims we hold ourselves to
Role-first, not exam-first
We teach the job — the patterns and techniques real teams use, including ones the exam never tests. The certification is a byproduct of competence, not the goal.
One coherent system
A single project — FieldNote — grows from a bare schema to an AI-enabled production system across the whole program. You finish with one substantial portfolio piece, not five disconnected demos.
Practitioner-led
Designed and taught by working database engineers, not career instructors. Every lesson is grounded in decisions you actually face on the job.
Lab-first AI
You work in real labs from day one — Docker dev containers via GitHub Codespaces (Microsoft) or LocalStack (AWS) — with minimal managed-cloud spend. Skills you build by doing, not by watching.
The throughline project
One project, start to production
FieldNote is a field-service SaaS application you build across the entire program. It starts as a bare schema and grows — through real design decisions about temporal data, encryption, indexing, window functions, fuzzy matching, and natural-language-to-SQL — into an AI-enabled production system.
- Build — schema, queries, programmability
- Harden — security, performance, CI/CD, observability
- AI-Enable — embeddings, vector search, hybrid retrieval, RAG
- Defend — close gaps, capstone defense, sit the exam
You finish with one substantial portfolio piece — not five disconnected demos. Read the FieldNotes →
How it works
Self-paced learning, flipped-classroom practice
1
Learn at your pace
Focused lessons of 20–45 minutes — a written explanation, worked examples with real code, and a hands-on exercise you do in your lab. Bite-sized professional learning, not academic lectures.
2
Practice in real labs
Work in Docker dev containers via GitHub Codespaces (Microsoft) or LocalStack (AWS). The same environment every time, minimal cloud spend, and code you keep. Sign in with GitHub — you’ll use it for labs anyway.
3
Show up and defend it
Once per module, a 60–90 minute live session on Zoom turns theory into judgement. Mini-capstones make you demonstrate capability — and the capstone is defended, not just submitted. Recordings are posted for on-demand review.
The journey across the program: Build → Harden → AI AI-Enable → Defend → Specialize.
Two editions, one program
Choose your platform
The same AI-Enabled Database Developer program, taught on the stack you work in.
Available now
Microsoft SQL Edition
AIDD-MS
Take a competent SQL user to an AI-enabled database developer on SQL Server & Azure SQL. Schema design, advanced T-SQL, security and deployment, and AI capabilities inside the database.
- Credential: Microsoft Certified: SQL AI Developer Associate (DP-800)
- 5 sub-courses · ~95 lessons · ~95–110 hours
- SQL Foundations → Database Development → Security & Deployment → AI Capabilities → Capstone
In development
AWS Edition
AIDD-AWS
The same role, built on AWS — Aurora PostgreSQL, DynamoDB, S3, OpenSearch, and Amazon Bedrock. Spans two AWS roles and earns two credentials, by design.
- Credentials: AWS Data Engineer – Associate (DEA-C01) + optional GenAI Developer – Professional (AIP-C01)
- 6 sub-courses · ~120 lessons · ~140 hours (est.)
- Foundations → Modeling & Ingestion → Operations & Security → AI Capabilities → Capstone → AI Specialization
Don’t guess where to start.
Take the free placement assessment. In a few minutes it tells you whether to begin with Foundations or jump straight into Database Development — and emails you a personalized starting point. No account required to start.