Apache Airflow Benefits and Best Practices | Quick Guide

What is Apache Airflow?

Apache Airflow is an open-source solution for programmatically authoring, scheduling, and monitoring processes. Data engineers use it as one of the most reliable platforms for coordinating pipelines or operations. Your data pipelines' dependencies, progress, logs, code, trigger jobs, and success status may all be readily visualised.

Users can create workflows using Airflow as Directed Acyclic Graphs (DAGs) of tasks. Thanks to Airflow's robust user interface, visualising production pipelines, tracking developments, and resolving problems as necessary is simple. Airflow can send an email or Slack alert when a task is finished or unsuccessful and connect to various data sources. Because of its flexibility, scalability, and distribution, Airflow is ideally suited to coordinating intricate business logic.

Apache Airflow primarily manages system workflows. As an open-source project initially developed by Airbnb in 2014, it remains in the incubator stage. The tool has garnered a strong reputation with around 500 contributors and 8,500 stars on GitHub.

How does Apache Airflow work?

It accomplishes the tasks by taking DAG(Directed Acyclic Graphs) as an array of workers; some of these workers have particularized contingencies. This results in the formation of DAGs in Python, making them DAGs easy to use for other processes. This results in the changing of a workflow into well-defined code, which further makes a workflow testable, maintainable, Cooperative, and Versionable.

DAG(Directed Acyclic Graphs)

In computer science and mathematics, a directed acyclic graph (DAG) is a directed graph that has no directed cycles. In graph theory, a graph refers to a set of vertices connected by lines called edges. In a directed graph, each edge is associated with a direction from a beginning vertex to an end vertex. If we travel along the edges' direction and find that no closed loops are formed along any path, it is said that there are no directed cycles. The graph formed is a DAG

Apache Airflow Architecture

Apache Airflow is a platform for programmatically defining, scheduling, and monitoring workflows. It uses a directed acyclic graph (DAG) to represent the workflow, which is a collection of tasks that are executed in a specific order

Various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data. Click to explore about, Open Source Big Data Tools

During all of the above procedures, tasks are not permitted to exchange the data, but with this fact, it is also true that metadata is transferred. It’s not considered a streaming solution for data. The working process of Apache Airflow is not likely to be similar to "Spark Streaming" or "Storm" space. However, it can be taken as similar to the "Azkaban" or Oozie.

Benefits of Apache Airflow

• Dynamic - The pipeline is constructed by an airflow dynamic, constructed in code, which gives an edge to be dynamic.

• Extensible - Another good thing about working with Airflow is that it is easy to initiate the operators and executors, which boosts the library to the level of abstraction needed to support a defined environment.

• Elegant - A pipeline developed with the help of Airflow is angular and unambiguous because Jinja template engine used to parameterize the scripts built into the core of Airflow.

• Scalable - The architecture of Airflow composed of standardized units which also use messaging technique for queuing the number of workers and moreover it is scalable to infinity.

A framework that allows storing large Data in distributed mode and allows for the distributed processing on that large datasets. Click to explore about, Apache Hadoop

Importance of Apache Airflow

These are the main reasons which signify the Importance of Apache Airflow -

• The most important advantage is that it provides the power to schedule the analytics workflow and Data warehouse, which are also managed under a single roof so that a comprehensive view can be accessed to check the status.

• The execution log entries are concentrated in one location.

• Airflow also matters as it is strong at automating workflow development and configuring the workflow as code.

• It can also send a reporting message through Slack if an error occurs due to DAG failure.

• Within the DAGs, it provides a clear picture of the dependencies.

• The ability to generate the metadata gives an edge in regenerating distinctive uploads.

A framework for storing large Data in distributed mode and distributed processing on that large datasets. Click to explore about, Apache Hadoop Benefits and Working

Steps to integrate Apache Airflow

To integrate Apache Airflow into your workflow, follow these structured steps:

1. Prerequisites

• Python Environment: Ensure you have installed Python 3.8 or higher, as Airflow requires this version for installation and operation3.

• Database Setup: Choose a backend database (e.g., PostgreSQL, MySQL) for production use, as the default SQLite is suitable only for testing.

2. Installation

• Set Airflow Home: Optionally set an environment variable for your Airflow home directory:

export AIRFLOW_HOME=~/airflow

• Install via pip: Use the following command to install Airflow with constraints:

pip install apache-airflow --constraint 
"https://raw.githubusercontent.com/apache/airflow/constraints-
2.7.0/constraints-3.8.txt"

Replace the URL with the appropriate constraints file for your Python version

3. Initialize Database

Run the following command to initialize the database:

airflow db init

4. Create a User

Create an admin user with:

airflow users create \
    --username admin \
    --firstname Peter \
    --lastname Parker \
    --role Admin \
    --email spiderman@superhero.org

5. Configure Connections

• Access the Airflow UI by navigating to localhost:8080 in your web browser.
• Under Admin -> Connections, add necessary connections to external systems (e.g., databases, APIs) using the UI
  

6. Define DAGs and Operators

• Create Directed Acyclic Graphs (DAGs) in Python to define your workflows.
• Use operators like PythonOperator, BashOperator, etc., to specify tasks within your DAGs
   

7. Run Airflow Components

• Start the web server and scheduler:

airflow webserver --port 8080
airflow scheduler

8. Testing and Monitoring

• Test your DAGs using the Airflow UI and monitor their execution.

• Set up logging and monitoring to track performance and resource usage

9. Scaling Out (Optional)

• For larger workloads, consider using the Celery or Kubernetes executors to distribute task execution across multiple workers

By following these steps, you can effectively integrate Apache Airflow into your data workflow management system, allowing for efficient scheduling and monitoring of complex workflows.