Inpsired by https://github.com/bereketkibru/Data_engineering_sensor_data

Using a docker-compose file, developed a completely dockerized ELT pipeline with MySQL for data storage, Airflow for automation and orchestration, DBT for data transformation, and a Redash dashboard connected to the MySQL database.

Tech Stack used in this project

• MYSQL
• Apache Airflow
• dbt
• Redash

Make sure you have docker installed on local machine.

• Docker
• DockerCompose

1. Clone the repo

   git clone https://github.com/pyjavo/update_csv_pipeline

2. Create directory /data at the root of the project.

3. Save file archivo.csv within /data directory.

4. Build

    docker-compose build

5. Create DB for server service

   docker-compose run --rm server create_db

6. Run

    docker-compose up

7. Open Airflow web browser

   Navigate to `http://localhost:8000/` on the browser
   use `admin` for username
   use `admin` for password

8. Access redash dashboard

   Navigate to `http://localhost:5000/` on the browser

9. Access your MySQL database using adminer

   Navigate to `http://localhost:8080/` on the browser
   choose mysql databse
   use `root` for username
   use `root` for password
   use `mysqldb` for database

Recommended docstring format is Google format

Distributed under the MIT License. See LICENSE for more information.