Build a Cloud Python Data Pipeline with AI (No Install Required)

Xuebin Wei
18 hours ago
4 min read

Is this how we will code in 2026? For decades, the first step in any Python data project was the same tedious dance: installing Python locally, managing virtual environments, dealing with package conflicts, and handling operating system differences. "It works on my machine" became the developer's infamous catchphrase.

GitHub Codespaces + Copilot: Cloud-Based AI-Assisted Python Data Analysis

Today, that era is ending. In this tutorial, based on the video above, we are ditching the local environment entirely. We will build a complete end-to-end Cloud Python Data Pipeline—from collecting Twitter (X) data to storing it in MongoDB and visualizing user networks—all within the browser using GitHub Codespaces and GitHub Copilot.

The best part? Throughout this entire complex project, I didn't type a single line of Python code myself. I let the AI do the heavy lifting.

Let's step into the future of development.

The New Toolkit: Codespaces and Copilot

Before we dive in, let's define our tools.

1. GitHub Codespaces: Think of this as your development computer running in the cloud. It gives you a fully configured Visual Studio Code environment right in your browser. It uses containers, so your environment is consistent every single time you launch it.

Note on Pricing: GitHub is generous here. Every GitHub user gets 120 core hours of Codespaces per month, which is plenty for student projects and learning. You can check out the detailed GitHub pricing plans here.

2. GitHub Copilot: This is your AI pair programmer. We are specifically using the new free tier of Copilot, leveraging its "Agent Mode."

Note on Pricing: The new free tier allows for 2000 completions per month. If you need unlimited completions or want to see the full power of the "Coding Agent" before upgrading, check out our previous tutorial: GitHub AI Coding Agent Demo. You can also compare the Copilot plans here.

Step 1: Setting Up Your Cloud Python Data Pipeline

We start by creating a standard GitHub repository. When setting this up, select a Python .gitignore template and an MIT license.

Once the repo is created, clicking the green "Code" button gives you the option to "Create codespace on main." Within minutes, a full VS Code editor loads in your browser tabs.

GitHub Codespaces VS Code interface in the browser.

Securing Your Credentials

We need to connect to external services (MongoDB and Twitter). Never hardcode passwords or API keys directly into your code.

In Codespaces settings, you can add "Secrets." These act as environment variables that your code can access securely. We added secrets for:

MONGO_CONNECTION_STRING: Our connection to a free MongoDB Atlas cluster. (If you are new to this, check out our Database Data Collection Course on LBSocial for a complete guide.)
TWITTER_BEARER_TOKEN: Our access key for the Twitter/X API. (If you don't have one, watch this guide on how to get a Twitter API key.)

Step 2: The AI Agent Workflow

This is where the magic happens. Instead of opening a .py file and typing import pandas, we open the Copilot Chat window in the sidebar.

We switched Copilot to Agent Mode. We then gave it a high-level command:

"Create python code to collect 100 tweets with the keyword 'generative AI' and store them into the MongoDB database demo.tweet_collection. Use the credentials stored in the environment variables."

GitHub Copilot Agent Mode writing Python code from natural language

The AI agent didn't just write the code block. It recognized it needed libraries like pymongo and requests, created a requirements.txt file, asked for permission to install them via pip, wrote the Python script, and then asked to run it.

Python script and requirements file automatically created by GitHub Copilot shown in file explorer.

The result? 100 fresh tweets inserted into our cloud database, untouched by human hands.

Step 3: Analysis and Visualization

We continued this conversational process to analyze the data in three different ways.

1. Network Analysis First, we asked the agent to build a network graph of user mentions using the networkx library to see who was talking to whom.

Python NetworkX graph visualization of Twitter user mentions.

2. Sentiment Analysis Next, we wanted to know the mood of the conversation. We asked the agent to perform sentiment analysis using the nltk library and visualize the results.

Matplotlib bar chart showing sentiment analysis results.

3. Text Summarization Finally, we asked the AI to read all the tweets and generate a text summary, saving it to a text file so we could quickly understand the trending topics.

The AI handled all library installations and code generation for these distinct tasks without any manual intervention.

Step 4: Deployment with GitHub Pages

Finally, we wanted to share our findings. We asked the AI to update the README.md file with a project summary and embed the generated charts into an index.html file.

Once the AI committed these changes to the repository, we went to the repository "Settings" -> "Pages" and deployed the main branch. Within moments, our repository was turned into a live public website showcasing our data analysis report.

Final HTML report deployed on GitHub Pages, showing charts and a summary

Conclusion: The Shift in Coding

In this entire process, my role shifted from "coder" to "architect." I defined what I wanted to happen and ensured the AI had the correct security permissions. The AI figured out the syntax to make it happen.

This cloud-native, AI-assisted workflow is faster, cleaner, and reproducible on any computer with a web browser.

Ready to master data collection?

Don't stop here. If you want to build a solid foundation in managing data for your AI projects, take our comprehensive Database Data Collection Course on LBSocial.