Build a Real Data Pipeline with Google Cloud Storage & Python 🚀 | End-to-End Project
🚀 In this video, we build a complete end-to-end data pipeline using Google Cloud Storage (GCS) and Python.
If you're a Data Analyst or aspiring Data Engineer, this is exactly how real-world data workflows look — not just theory.
🎯 What you’ll learn:
Upload data to Google Cloud Storage using Python
Read files directly from GCS into pandas
Clean and transform data (real-world scenario)
Upload processed data back to GCS
Understand raw vs processed data architecture
🧠 Architecture Covered:
Local CSV → GCS (input/) → Python Processing → GCS (processed/)
💻 Technologies Used:
Google Cloud Storage (GCS)
Python
Pandas
📂 Project Flow:
1. Upload raw data to GCS
2. Read data using Python
3. Clean & transform dataset
4. Store processed data separately
🔥 Why this matters:
This is how real data pipelines start in companies. Mastering this will help you in Data Analyst & Data Engineering roles.
📌 Previous Videos in Playlist:
GCS Basics
Upload & Download using Python
GCS using CLI
00:00 Introduction and Concept Overview
00:37 Project Prerequisites
01:21 Setting Up Your Development Environment
01:50 Creating a Service Account & JSON Key
04:14 Installing Python Libraries
05:07 Creating a GCS Bucket
06:54 Setting Up IAM Permissions
07:42 Uploading a Local CSV File to GCS
12:47 Verifying File Upload in GCS
12:54 Reading the CSV File Directly from GCS
16:21 Data Cleaning and Transformation with Pandas
18:01 Uploading Processed Data Back to GCS
20:41 Verifying Final Output and Conclusion
👉 Next Video:
Automate this pipeline using Cloud Functions (don’t miss this)
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