A First End-to-End Pipeline

Concepts covered: paEndToEndPipeline, paRawZone, paHighWaterMark

Vocabulary becomes useful when applied to a concrete case. Take a small subscription product that wants a daily report of new signups by country. The data exists. The app records every signup to a Postgres table. The marketing team wants a chart on Monday morning showing last week's daily numbers, broken out by country. There is no pipeline. The work below builds one, end to end, with each role visible. Step 1: Identify the Source The source is the Postgres signups table. It has many columns; the pipeline needs only three: signup_timestamp, country_code, and user_id. The pipeline must not query Postgres at peak traffic, so it runs at 2am Pacific when load is lowest. It must not download the whole table every day, so it pulls only signups since the last successful run. That last constraint

About This Interactive Section

This section is part of the What a Data Pipeline Is: Beginner lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.