Airflow Tutorial for Beginners

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Introduction

Apache Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows.
It is particularly useful for orchestrating complex data pipelines and is widely used in data engineering and data science.
Airflow allows users to define workflows as Directed Acyclic Graphs (DAGs) of tasks, which can be scheduled to run at specified intervals.
The platform is highly extensible, with a wide range of pre-built operators and the ability to create custom plugins.
Airflow's architecture includes components like the Scheduler, Executor, Webserver, and Metadata Database, which work together to manage and execute workflows.

Tasks and DAGs: Airflow's core components are tasks and Directed Acyclic Graphs (DAGs), which define the workflow structure.
Scheduler: Responsible for scheduling tasks and managing their execution based on defined intervals.
Executor: Allocates tasks to be run by configured resources, ensuring efficient execution.
Webserver: Provides a user interface for monitoring and managing workflows, offering visibility into pipeline execution.
Extensibility: Airflow supports a wide range of operators and allows for custom plugin development to extend its functionality.

Pip Installation: Requires Python3 and involves setting up the Airflow home directory and configuration files manually.
Docker Compose: Simplifies installation by using a pre-configured Docker Compose file to set up Airflow components.
Astro CLI: A tool provided by Astronomer that automates the setup of a local Airflow development environment.
Prerequisites: Both Docker and Python3 are required for most installation methods.
Configuration: After installation, users need to configure Airflow settings, such as the metadata database and executor type.

DAG Definition: Use the DAG function from the airflow module to define a DAG with parameters like start_date and schedule.
Task Creation: Define tasks using operators such as PythonOperator, which execute Python functions.
Dependencies: Set task dependencies using bit shift operators or by passing data between tasks using XComs.
TaskFlow API: A newer method to define tasks using decorators, simplifying the process of creating DAGs.
Execution: Once defined, DAGs can be executed and monitored through the Airflow UI.

Modularity: Break down workflows into smaller, manageable tasks to simplify troubleshooting and improve performance.
Determinism: Ensure that DAGs produce consistent results by avoiding non-deterministic operations.
Idempotency: Design tasks to be idempotent, meaning they can be run multiple times without adverse effects.
Orchestration vs. Transformation: Use Airflow for orchestration and delegate heavy data transformations to specialized tools.
Monitoring: Regularly monitor DAGs and tasks through the Airflow UI to identify and resolve issues promptly.

ETL Pipelines: Airflow is commonly used to orchestrate Extract, Transform, Load (ETL) processes in data engineering.
Machine Learning: Automate machine learning workflows, including data preprocessing, model training, and deployment.
Data Integration: Integrate data from various sources and manage dependencies between different data processing tasks.
Analytics Dashboards: Schedule and automate data extraction and transformation for analytics dashboards.
Cloud Operations: Manage cloud-based workflows, such as data transfers between cloud storage and data warehouses.