Skip to content
Deadline to Die

Beginner's Guide: Step-by-Step Data Engineering

👷 Beginner’s Guide: Step-by-Step Data Engineering

Data Engineering is about building the “Plumbing” of the data world. Follow these 6 steps to go from a developer to an ETL specialist.

🟦 Step 1: SQL Mastery

SQL is 80% of the job. You must move beyond simple SELECT statements.

  • Learn: Joins, Aggregations, Subqueries.
  • Master: CTEs (Common Table Expressions) and Window Functions (RANK, LEAD, LAG).

✅ Goal: Write a single query that calculates the 7-day moving average of sales.

🟨 Step 2: Python for ETL

Use Python to fetch data from APIs and clean it.

  • Libraries: requests (API), pandas (Transformation), pydantic (Validation).
  • Tools: Use uv or poetry for environment management.

✅ Goal: Build a script that fetches weather data from an API and saves it to a CSV file.

🟧 Step 3: Data Modeling

Learn how to structure data so it is easy to query.

  • OLTP vs. OLAP: Databases for apps vs. Databases for analytics.
  • Star Schema: Understanding Facts and Dimensions.

✅ Goal: Design a simple database schema for an E-commerce store.

🟥 Step 4: Storage & File Formats

Data isn’t just in databases. It lives in files.

  • Formats: CSV vs. Parquet vs. JSON.
  • Cloud: Learn basic S3/Azure Blob Storage concepts.

✅ Goal: Convert a 1GB CSV file into Parquet and compare the file size and read speed.

🟪 Step 5: Orchestration Basics

Data pipelines shouldn’t be run manually.

  • Concepts: CRON jobs, Retries, and Error handling.
  • Tools: Start with a simple Python library like schedule or Prefect.

✅ Goal: Schedule your weather script to run every hour and send an alert if it fails.

🚀 Step 6: Build your first Pipeline

Combine everything into a “Portfolio Project.”

  • Project: API -> Python -> Postgres -> Dashboard.