I Built My First ETL Pipeline as a Complete Beginner. Here’s How.

2026-05-25 · Source: Towards Data Science · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering · Depth: Novice, medium

Summary

An individual documented building their first Extract, Transform, Load (ETL) pipeline from scratch using Python. This project involved extracting data on the 30 most starred Python repositories created after 2025-04-22 from the GitHub API using the `requests` library. The extracted raw JSON data was then transformed into a Pandas DataFrame, where specific fields like `name`, `owner`, `stars`, `forks`, `language`, `description`, `url`, and `created_at` were selected. Further transformations included dropping one row with a missing description and adding a "viral" column for repositories exceeding 50,000 stars. Finally, the cleaned 29-row, 9-column dataset was loaded into a CSV file named `github_trending_repos.csv`. The author highlights the profound learning experience gained from practical application compared to theoretical study.

Key takeaway

For aspiring data engineers or AI students building foundational skills, prioritize hands-on project work over endless tutorials. Your first ETL pipeline can be built simply with Python, `requests`, and `pandas` to extract, transform, and load real data from an API into a CSV. This practical application will solidify core concepts like API interaction and data manipulation far more effectively than theoretical consumption, preparing you for more complex orchestration tools.

Key insights

Building teaches more than consuming theory, especially for data engineering fundamentals.

Principles

• ETL is fundamental: Extract, Transform, Load.
• Start simple with pure Python for core concepts.
• Hands-on building accelerates learning.

Method

The proposed method involves using Python's `requests` library to extract API data, `pandas` to transform it (select fields, clean, add columns, sort), and `to_csv` to load the output.

In practice

• Use `requests.get()` for API extraction.
• Convert API response with `.json()`.
• Use Pandas for data cleaning and shaping.

Topics

• ETL Pipelines
• Python Data Engineering
• GitHub API
• Pandas DataFrames
• Data Extraction
• Data Transformation

Code references

• anthropics/skills

Best for: Data Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.