Data Systems Academy

Learn Data Engineering, ML Engineering & GenAI by Building Real Systems

Master design, architecture, and operations through hands-on courses with real constraints, peer review, and ethical gamification.
Launching early Q3 2026.


pipeline:
  ingest:
    source: kafka
    schema: avro
    sla: 500ms
  transform:
    type: dbt
    materialization: incremental
  quality_gate:
    tests: [unique, not_null]
    action: block_on_fail

The Gap: Data Engineering Courses Teach Tools. Teams Need Engineers.

Learn to design, ship, and operate real systems—with constraints, gates, and peer review.

  • Build under SLA, cost, privacy, security constraints (not "toy projects").
  • Prove quality with tests, evaluation, and review standards.
  • Graduate with a versioned portfolio that reads like real work.

Production Reality Checklist

ADR & Architecture Diagram
Pipeline + Tests (unique/not_null)
Cost & Latency Budget Defined
Monitoring + Alert Thresholds
Incident Runbook + RCA
Peer Review Score > 80%

One Mission. End-to-End. Like Production Teams Work.

Design, ship, validate, review, operate—and walk away with proof.

Casebook
Constraints
Studio
Build
Gates
Automated
Guilds
Review
Portfolio
Proof

Outputs

  • Test Report
  • Performance Report
  • Eval Report

Signals

  • Tests Pass
  • p95 < 200ms
  • Groundedness > 0.8

Constraints

  • Cost per Run
  • Latency Budget
  • PII Checks
Example Mission: Listing Monitoring System
E-CommerceData Quality
Artifacts Produced
ADR
Code
Dashboard
RCA
Gates Passed
UniqueNotNullSLA
Outcome
Portfolio Entry

Pick Your Data Engineering, ML Engineering, or GenAI Track.

Each track ends with a shipped system and a reviewed portfolio entry.

Data Engineering

Ship a production-grade ingestion + transformation + serving pipeline.
ArtifactsADR, dbt project, DAG, Dashboard
GatesDQ tests, Cost budget, Latency
EMPLOYERS WILL SEE:
  • PR-reviewed system repo
  • Architecture decisions
  • Incident RCA

ML Engineering

Ship a training + feature pipeline + deployment + monitoring system.
ArtifactsModel Card, Feature Store, Rollback Plan
GatesDrift checks, Offline Eval, Safety
EMPLOYERS WILL SEE:
  • Training pipeline code
  • Model evaluation report
  • Drift monitoring dashboard

GenAI Apps

Ship a RAG/agent app with evaluation, guardrails, and cost controls.
ArtifactsEval Harness, Golden Set, Guardrails
GatesGroundedness, Latency, Cost/Query
EMPLOYERS WILL SEE:
  • Eval harness results
  • Guardrail policy code
  • Cost optimization report

DSA Isn't Content. It's a Quality System.

We don't rely on motivation. We rely on mechanisms.

Learn by Building

Every unit produces a tangible artifact. No theory without context.

Real Constraints

SLA, cost, privacy, scale. Design tradeoffs like in production.

Feedback First

Automated gates + human peer review with detailed rubrics.

Portfolio Proof

Your versioned, reviewed system portfolio is your credential.

The DSA Engine

Automated Gates
DQ Tests, Perf Budgets
Human Review
Rubrics + Feedback
Ops Muscle
Incidents + RCAs
Ethical Game
Badges, No Penalties

Get Beta Access + Founding Discounts

Answer a few questions so we can build the right missions—and we'll prioritize your invite.

Quick entry: 30s Optional survey: ~3m
1
Fast Entry
2
Deep Survey (Optional)

Roadmap

Discovery + Building

Now

Beta Launch (MVP)

Early Q3 2026

Studio + Casebook

Q3 2026

Full Platform

Q4 2026+

Common Questions

Everything you need to know about the product, the method, and the beta.

Product & Learning Model

What is Data Systems Academy (DSA), exactly?

How is DSA different from online courses?

What does "One Mission. End-to-End." mean?

Who it's for

Is it suitable for beginners?

Do I need math or ML background?

Projects, Portfolio & Proof

What will I have in my portfolio after finishing?

Are projects "toy" or production-like?

Pricing & Plans

What is the cost?

When does beta launch?

Tech & Logistics

What stack do I need?

How much time per week?