Refactoring Neural Pathways: The 'Do-It-Again' Protocol for Data Engineering

We have all been there. You find a great 2-hour coding workshop on the external networks. You sit back, watch the architect type away, nod along as they explain concepts, and by the end, you feel like you’ve mastered the sector.

Then, you open your terminal to interface directly, and your mind goes blank.

Here is the hard truth: Watching is not learning. Watching is passive buffer consumption. True learning—the kind that rewires your neural pathways and builds muscle memory—only happens when you struggle through the logic yourself.

At the Yellow Capsule, we prioritize Project-Based Learning, but with a medical-grade twist: The “Do-It-Again” Protocol.

🧬 The Protocol

1. System Scan (Watch): Get the high-level architecture. Understand the “why” and the skeletal structure.
2. Neural Decomposition (Break it down): Don’t just copy-paste the code. List the logical operations required to reconstruct the project.
3. Logic Reconstruction (Rebuild): Close the external feed. Open your IDE. Try to build it using your list of tasks. Only access the backup (source code) if you are facing a total system crash.

🛠️ The “Do-It-Again” Checklist (Revised & Logically Ordered)

Phase 1: Preparation & Infrastructure

Before writing code, let’s get the tools and the storage ready.

Task 1: The Modern Setup

Stop using standard pip. The video introduces a faster, Rust-based package manager called uv.

• Env Setup: Set up a GitHub Codespace (or a Linux environment).
• Install Tool: Install uv.
• Init Project: Initialize a new Python project using uv init.
• Dependencies: Create a virtual environment and install pandas, sqlalchemy, and psycopg2-binary using uv add.

Task 2: Docker Warm-up

Prove to yourself how containers work.

• Hello World: Run the hello-world image.
• Interactive Mode: Run an ubuntu container interactively.
• The Stateless Test: Create a file inside the container, exit, and run it again. Verify the file is gone.

Task 3: Running the Database (The Storage)

We need a database running BEFORE we write the script, otherwise, we have nowhere to send the data.

• Run Postgres: Run a PostgreSQL container using docker run.
• Env Variables: Crucial Step: Use -e to set the user, password, and database name.
• Port Mapping: Map port 5432 on your host to 5432 in the container.
• Volume Mapping: Mount a volume to /var/lib/postgresql/data to ensure data persistence.
• Verification: Use a tool like pgcli or DBeaver on your local machine to confirm you can connect to this database on localhost:5432.

Phase 2: Development (Local)

Now that the database is running, we write the logic locally first.

Task 4: The Ingestion Script (The Logic)

• Create Script: Write a Python script (ingest_data.py) that accepts a URL for the NYC Taxi CSV dataset.
• Pandas Logic: Use Pandas to read the CSV. Challenge: Do not read the whole file at once. Use the chunksize iterator to process 100,000 rows at a time.
• DB Connection: Use SQLAlchemy to connect to your locally running Postgres container (host=localhost).
• Data Insertion: Generate the schema (.head(0).to_sql) and then loop through chunks to insert data (.to_sql(if_exists='replace')).
• Verify: Verify data is inside the DB using your SQL client.

Phase 3: Containerization & Orchestration

The script works locally. Now, let’s put the script inside a container and make them talk.

Task 5: Dockerizing the Script

Package your Python code so it runs anywhere.

• Dockerfile: Create a Dockerfile. Start with a python:3.9-slim image (or similar).
• Build Steps: COPY project files and RUN dependency installation.
• Entrypoint: Set the ENTRYPOINT to python.
• CLI Refactor: Use the click library to make the script accept command line arguments (host, user, password, etc.).

Task 6: Networking (Connecting Containers)

Crucial concept: localhost inside a container means “me”, not “my laptop”.

• Create Network: Create a custom Docker network: docker network create pg-network.
• Re-run Postgres: Stop and remove your old Postgres container. Run a new one attached to this network.
• Build Image: Build your ingestion script image (docker build).
• Run on Network: Run your ingestion script container on the same network.
• The Fix: Pass the Postgres container name as the host argument to your script (replacing localhost).

Task 7: The Final Orchestration (Docker Compose)

Stop running long commands manually.

• Create Compose: Create a docker-compose.yaml file.
• Define Services: Define pgdatabase (Postgres) and pgadmin (GUI).
• Config: Configure networks, ports, volumes, and environment variables in the YAML.
• Launch: Run docker-compose up.

Task 8: End-to-End Verification

• Check Status: Ensure docker-compose is running.
• Run Ingestion: Run your dockerized ingestion script (Task 5 image) on the Compose network.
• PgAdmin Check: Open pgAdmin in your browser (localhost:8080), connect to the Postgres service using its service name, and query the table.

🧪 Post-Op Analysis

Why do this?

When you watch, you understand the syntax. When you build, you understand the system.

You will encounter Connection Refused errors. You will misalign your volume paths. Good. Those errors are where the neural rewiring actually happens. Those glitches are your brain learning to debug the matrix.

Close the video feed. Open VS Code. Start Phase 1.

Happy building, initiate.