📅 Data Engineering - Modeling

This repository will

TL;DR

1. Clone the repository.
2. Start Postgres instance.
3. Connect to Postgres using your preferred database management tool.

For detailed instructions and more information, please refer to the step-by-step instructions below.

1️⃣ Clone the repository

• Clone the repo using the SSH link. This will create a new folder in the current directory on your local machine.

  git clone [email protected]:knands42/Data-Engineering-Modeling.git

• Navigate into the cloned repo using the command line:

  cd Data-Engineering-Modeling

2️⃣ Run Postgres

There are two methods to get Postgres running locally.

💻 Option 1: Run on local machine

1. Install Postgres

   • For Mac: Follow this tutorial (Homebrew is really nice for installing on Mac)
   • For Windows: Follow this tutorial

2. Run this command after replacing <computer-username> with your computer's username:

   psql -U <computer-username> postgres < data.dump

3. Set up DataGrip, DBeaver, or your VS Code extension to point at your locally running Postgres instance.

4. Have fun querying!

🐳 Option 2: Run Postgres in Docker

• Install Docker Desktop from here.

• Copy example.env to .env:

  cp example.env .env

• Start the Docker Compose container:

  • If you're on Mac:

    ```bash
    make up
    ```
    

• A folder named postgres-data will be created in the root of the repo. The data backing your Postgres instance will be saved here.

• You can check that your Docker Compose stack is running by either:

  • Going into Docker Desktop: you should see an entry there with a drop-down for each of the containers running in your Docker Compose stack.
  • Running docker ps -a and looking for the containers with the name postgres.

• When you're finished with your Postgres instance, you can stop the Docker Compose containers with:

  make down

💡 Additional Docker Make commands

• To restart the Postgres instance, you can run make restart.
• To see logs from the Postgres container, run make logs.

Credits

This project was created to follow the lessons and solve exercises from Data with Zach bootcamp on YouTube.