Overview

Welcome to Astronomer! This project was generated after you ran 'astro dev init' using the Astronomer CLI. This readme describes the contents of the project, as well as how to run Apache Airflow on your local machine.

Project Contents

Your Astro project contains the following files and folders:

• dags: This folder contains the Python files for your Airflow DAGs. By default, this directory includes one example DAG:
  • example_astronauts: This DAG shows a simple ETL pipeline example that queries the list of astronauts currently in space from the Open Notify API and prints a statement for each astronaut. The DAG uses the TaskFlow API to define tasks in Python, and dynamic task mapping to dynamically print a statement for each astronaut. For more on how this DAG works, see our Getting started tutorial.
• Dockerfile: This file contains a versioned Astro Runtime Docker image that provides a differentiated Airflow experience. If you want to execute other commands or overrides at runtime, specify them here.
• include: This folder contains any additional files that you want to include as part of your project. It is empty by default.
• packages.txt: Install OS-level packages needed for your project by adding them to this file. It is empty by default.
• requirements.txt: Install Python packages needed for your project by adding them to this file. It is empty by default.
• plugins: Add custom or community plugins for your project to this file. It is empty by default.
• airflow_settings.yaml: Use this local-only file to specify Airflow Connections, Variables, and Pools instead of entering them in the Airflow UI as you develop DAGs in this project.

Deploy Your Project Locally

1. Start Airflow on your local machine by running 'astro dev start'.

This command will spin up 4 Docker containers on your machine, each for a different Airflow component:

• Postgres: Airflow's Metadata Database
• Webserver: The Airflow component responsible for rendering the Airflow UI
• Scheduler: The Airflow component responsible for monitoring and triggering tasks
• Triggerer: The Airflow component responsible for triggering deferred tasks

2. Verify that all 4 Docker containers were created by running 'docker ps'.

Note: Running 'astro dev start' will start your project with the Airflow Webserver exposed at port 8080 and Postgres exposed at port 5432. If you already have either of those ports allocated, you can either stop your existing Docker containers or change the port.

3. Access the Airflow UI for your local Airflow project. To do so, go to http://localhost:8080/ and log in with 'admin' for both your Username and Password.

You should also be able to access your Postgres Database at 'localhost:5432/postgres'.

Deploy Your Project to Astronomer

If you have an Astronomer account, pushing code to a Deployment on Astronomer is simple. For deploying instructions, refer to Astronomer documentation: https://www.astronomer.io/docs/astro/deploy-code/

Contact

The Astronomer CLI is maintained with love by the Astronomer team. To report a bug or suggest a change, reach out to our support.

Setup Airflow Data Warehouse (draft)

1. Clone the repository from GitHub:

   git clone https://github.com/gregomelo/airflow-datawarehouse.git

2. Assure you have Astro CLI install in your system.

   For this, try the comamand astro at your terminal.

   If an error is raised, install Astro: curl -sSL https://install.astronomer.io | sudo bash

3. Start the Astro Airflow.

astro dev start

New Dags

If you want to dev new dags, you just need to create then at dags/ folder and wait some instantes to see then at Airflow UI.

New Extractors or Tools

If you want to dev new extractors or tools to use in dags, we recommended you to isolate your enverionments.

1. Ensure you have pyenv and Poetry installed on your system for dependency management.

2. Ensure you have the python version 3.11.11 avaiable in your system using the command pyenv versions. If 3.11.11 is not listed, use the command pyenv install 3.11.11.

3. Navigate to the cloned directory and install the dependencies using Poetry:

   cd airflow-datawarehouse
   
   pyenv local 3.11.11
   
   poetry env use 3.11.11
   
   poetry install --no-root --with dev
   
   poetry lock --no-update

Local Tests with Pytest

There are two ways to test this project:

1. Running only the test container.

   For this, try astro dev pytest.

   For more details about this option look Astro documentation.

2. Running all services.

   astro dev start
   astro dev bash
   pytest

   This code will open a terminal on scheduler container and you can run your tests, include DAG tests.