Data & Analytics

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Data Engineering

A hands-on path from Python and SQL foundations to warehousing, pipelines, orchestration, and a production-style capstone data pipeline.

Finish line: Capstone: End-to-End Data Pipeline

How to use your map

  • • Work top to bottom — each stage builds on the one before it.
  • • Complete a topic to unlock what it leads to.
  • • When unsure, start at your highlighted next best step.
✓ done▢ next best step (ringed) locked — finish its prerequisites first
Your progress0 / 11 done · 0%

~84 hr 30 min left · about 169 days at 30 min/day

1Start here

Python for Data

skill

Nearly every data pipeline glue task, extraction script, and transformation in data engineering is written in Python.

~480 min·difficulty 2/5
2Stage 2

SQL & Databases

skill

SQL is the lingua franca of data engineering — it drives warehouse transformations, data checks, and analytics.

~600 min·difficulty 2/5
3Stage 3

ETL vs ELT

concept

The ETL-vs-ELT choice shapes your entire pipeline architecture and tooling decisions.

~120 min·difficulty 2/5

Data Modeling & Warehousing

concept

Well-designed dimensional models make a warehouse fast, intuitive, and cheap to query.

~300 min·difficulty 3/5
4Stage 4

Building Data Pipelines

skill

Assembling reliable, repeatable pipelines is the core day-to-day work of a data engineer.

~720 min·difficulty 3/5

Cloud Data Basics

concept

Modern data engineering runs on cloud services, and understanding their building blocks is essential to design and cost pipelines.

~300 min·difficulty 2/5
5Stage 5

Batch vs Streaming

concept

Choosing batch vs streaming determines latency, infrastructure complexity, and cost of a data system.

~150 min·difficulty 3/5

In-Warehouse Transformation with dbt

skill

dbt is the standard tool for the 'T' in ELT, turning raw warehouse tables into tested, documented analytics models.

~420 min·difficulty 3/5
6Stage 6

Workflow Orchestration with Airflow

skill

Orchestrators like Airflow turn ad-hoc scripts into scheduled, monitored, dependency-aware pipelines.

~480 min·difficulty 4/5
7Stage 7

Data Quality & Testing

skill

Silent bad data is worse than a failed job; automated quality checks keep downstream analytics trustworthy.

~300 min·difficulty 3/5
8Stage 8

Capstone: End-to-End Data Pipeline

project

A complete, portfolio-grade pipeline proves you can integrate every skill into a working system.

~1200 min·difficulty 5/5