Apache Airflow How To

Introduction

Apache Airflow is a platform for programmatically control the execution of an Airflow workflow. It's a vital tool for data engineers and data scientists.

Topics

• 1. Workflows: Defining your data pipelines.
• 2. Tasks: The core of your workflows.
• 3. Operators: Automating complex actions.
• 4. Connections: Connecting to various data sources.
• 5. DAGs: Data DAGs – the blueprint of your pipeline.

Key Concepts

- DAGs: Define a sequence of tasks (operators) to perform. - Operators: Automate tasks like data transformation, scheduling, or monitoring. - Tasks: Execute specific operations within a DAG.

Example: Simple Workflow

Let's create a basic workflow that extracts data from a CSV file and writes it to a new CSV.

1. Create a `data_extractor.py` file with: ```python import pandas as pd def extract_data(input_file): df = pd.read_csv(input_file) return df ```

2. Create a `data_pipeline.py` file with: ```python from airflow.providers.osw.operators.csv import CSVOperator from airflow.providers.osw.operators.data_operator import DataOperator import airflow.providers.osw.operators.data_operator.DataOperator def main(): data = CSVOperator( input_file='data/input.csv', output_file='data/output.csv', data_type='float' # Adjust data type as needed ) print("Workflow started.") return ```

Resources

- [Apache Airflow Documentation](https://airflow.apache.org/) - [Airflow Community](https://community.airflow.com/)