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title: Beginner Data Engineering Roadmap

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# Beginner Data Engineering Roadmap

This roadmap is designed for beginners to go from zero to entry-level data engineering skills through hands-on projects.

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## **Phase 1: Basics of Python & SQL**

**Goal:** Learn how to manipulate data and interact with databases.

**Skills to Learn:**

* Python basics (variables, loops, functions)
* Pandas library (data cleaning, filtering, transformations)
* SQL basics (SELECT, JOIN, GROUP BY, WHERE, INSERT, UPDATE)

**Project 1 – CSV to Database:**

* Take a CSV dataset (e.g., sales, movies, or sports stats)
* Clean the data using Pandas
* Load it into a PostgreSQL or MySQL database
* Run SQL queries to answer simple questions (e.g., top 5 products sold, average sales per month)

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## **Phase 2: ETL Fundamentals**

**Goal:** Automate extraction, transformation, and loading of data.

**Skills to Learn:**

* Python scripting for data pipelines
* SQL for data transformation
* Cron jobs or simple scheduling

**Project 2 – API Data Pipeline:**

* Fetch data from a free API (e.g., weather, COVID stats, cryptocurrency)
* Transform it to a clean format
* Load it into a database
* Schedule the script to run daily and update the database

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## **Phase 3: Data Warehousing & Modeling**

**Goal:** Organize data for analytics and reporting.

**Skills to Learn:**

* Dimensional modeling (fact and dimension tables)
* SQL joins and aggregations
* Designing star or snowflake schemas

**Project 3 – Sales Data Warehouse:**

* Design a small warehouse with tables like Customers, Orders, Products, and Sales Fact Table
* Populate the warehouse with mock or real CSV data
* Write queries for analytics: total sales per product, monthly revenue trends, top customers

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## **Phase 4: Automation & Pipelines**

**Goal:** Learn to automate workflows and handle multiple datasets.

**Skills to Learn:**

* Workflow automation tools (Airflow or Prefect)
* Modular Python scripts for ETL
* Logging and error handling

**Project 4 – Automated ETL Pipeline:**

* Automate the CSV/API pipelines from earlier projects
* Use Airflow to schedule daily or hourly runs
* Store logs for success/failure of each run

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## **Phase 5: Optional Cloud Introduction**

**Goal:** Learn cloud-based data engineering tools.

**Skills to Learn:**

* AWS (S3, Redshift, Glue) or GCP (BigQuery, Dataflow)
* Uploading and querying data on the cloud
* Serverless ETL pipelines

**Project 5 – Cloud ETL Pipeline:**

* Store raw CSV/API data in S3 or Google Cloud Storage
* Transform and load it into a cloud data warehouse
* Run queries on the cloud warehouse to generate reports

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## **Recommended Order for Beginners**

1. Project 1 – CSV to Database ✅
2. Project 2 – API Data Pipeline ✅
3. Project 3 – Sales Data Warehouse ✅
4. Project 4 – Automated ETL Pipeline ✅
5. Project 5 – Cloud ETL Pipeline (optional for advanced beginner)

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## **Tips for Beginners**

* Start **small**, don’t try to learn everything at once.
* Focus on **Python + SQL + basic ETL**, these are the core skills every data engineer must know.
* Document your projects on GitHub – this acts as your portfolio for entry-level jobs.

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## **Next Step**

Create a **“Project Cheat Sheet”** with specific datasets, step-by-step tasks, and skills for each project to start coding immediately.

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This Markdown roadmap can be used in Notion, GitHub, VSCode, or any Markdown editor.
