Start Here

Who this is for

You are the right reader if you are:

• A self-taught coder, analyst, or career-changer who can write a SELECT and a Python loop but keeps hitting words like CDC, idempotent, lakehouse, or orchestration and bouncing off.
• A data analyst who wants to move "down the stack" into the engineering that produces the tables you query.
• A software engineer curious about data infrastructure who wants the data-specific concepts, not another general programming course.
• A student who wants the job-relevant slice of a computer-science and data-systems education without the parts you'll never use.

You do not need a CS degree, a math background beyond high school, a powerful computer, or any paid software. Everything in this curriculum runs free on a normal laptop.

What you'll be able to do at the end

By the time you finish all eight courses, you will be able to:

• Explain how a database, a data warehouse, and a data lake actually work under the hood — and when to use each.
• Model data properly: normalize a transactional schema and design a dimensional model for analytics.
• Build pipelines that ingest from databases, files, APIs, and event streams — and survive late, duplicate, and out-of-order data.
• Use the core modern tools — git, Docker, dbt, an orchestrator, an object store, a warehouse — with real fluency.
• Stand up a small but complete data platform end to end (the capstone), and operate it: tests, monitoring, and a semantic layer.
• Reason about scale, cost, and trade-offs the way a senior engineer does — and talk about it in an interview.

That last point is the bridge into the Data Platform Systems Design guide, which is the senior-level "reading" at the end of this journey.

Prerequisite self-check

Try these two tiny problems. If you can do them (or read the solution and think "ah, of course"), you're ready. If they're total mysteries, spend a week on a free intro-SQL and intro-Python tutorial first, then come back.

-- Given a table `orders(id, customer_id, amount, created_at)`,
-- write a query for total amount spent per customer,
-- highest spenders first.

SELECT customer_id, SUM(amount) AS total_spent
FROM orders
GROUP BY customer_id
ORDER BY total_spent DESC;

# Given a list of dicts, return the average "amount",
# skipping any row where amount is missing (None).
rows = [{"amount": 10}, {"amount": None}, {"amount": 20}]

vals = [r["amount"] for r in rows if r["amount"] is not None]
average = sum(vals) / len(vals) if vals else 0
print(average)  # 15.0

How the curriculum works

Eight courses, each a guide in this topic, designed to be done in order:

1. Roadmap (you're here) — orientation only.
2. Orientation & Setup — the mental model + your hands-on environment.
3. Foundations — the computer-science and data-systems core, taught for this job.
4. The DE Craft — modeling, pipelines, dbt, streaming, orchestration, quality.
5. Tooling & the Modern Stack — git, Docker, cloud, CI/CD, the real toolbox.
6. Capstone Labs — build a working mini data platform, lab by lab.
7. Career & Getting the Job — portfolio, resume, interviews, the first 90 days.
8. Glossary & Cheat Sheets — a reference to keep open the entire time.

Every chapter follows the same rhythm, so you always know where you are:

Hands-on chapters add a Setup box (what to install/run) and "You should see…" checkpoints so you can confirm each step worked before moving on.

The learning contract

Four commitments that, in our experience, separate the people who finish from the people who don't:

• Type the code; don't copy-paste it. Your fingers learn what your eyes skip. Every runnable block is short on purpose.
• Do the exercises before reading the solution. The struggle is the learning. A solution you understand by reading is not a skill you have.
• Keep one companion repo. From Course 2 onward you'll build up a single git repository. By the end it's your portfolio.
• Ship something small every week. A query, a script, a pipeline. Momentum compounds; perfectionism kills.

Myths to drop on day one

✓ Check yourself

Before moving on, make sure you can answer "yes" to these:

• Could you do (or fully follow) both prerequisite self-checks?
• Can you name the eight courses and roughly what each covers?
• Do you understand the four-part rhythm every chapter follows?
• Have you decided when in your week you'll study (see Course 1 ch. 03)?

Next

Now that you know how this works, let's make the destination concrete: what a Data Platform Engineer actually does all day. → What a Data Platform Engineer Is