Apache Airflow is a workflow engine that efficiently plans and executes complex data pipelines. It ensures that each task in your data pipeline runs in the correct order and that each job gets the resources it needs.
It provides a friendly UI to monitor and fix any issues.
Airflow is a platform for programmatically creating, scheduling, and monitoring workflows.
Use Airflow to create a workflow as a directed acyclic graph (DAG) of tasks. A wide range of command line utilities makes it easy to perform complex operations on DAGs. The Airflow scheduler runs worker errands according to specified dependencies.
Airflow has based on Python, but you can run programs in any language. It helps automate scripts to perform tasks. For example, the first phase of the workflow requires running a C++-based program to perform image analysis and then a Python-based program to transfer that information to S3. The possibilities are endless.
Apache Airflow is a powerful scheduler for programmatically creating, scheduling, and monitoring workflows. You are built to handle and orchestrate complex data pipelines. Originally designed to solve the problems associated with lengthy cron jobs and heavy scripting, it has evolved into one of the most powerful data pipeline platforms.
A common challenge when developing big data teams is the need for more ways to organize related tasks into end-to-end workflows. Airflow is a platform for defining, executing, and monitoring workflows. A workflow can be defined as a series of steps toward a specific goal. Airflow had many limitations ahead of Oozie, but Airflow surpassed them with complex workflows.
Optimize your workflow management so you can perform thousands of tasks every day. Airflow is also a code-centric platform based on the idea that data pipelines are best expressed in code. Designed to be extensible, you can use plugins to interact with the venue and create as many standard external systems and media as you need.
Why should I use Apache Airflow?
Some of the many benefits. Apache Airflow does three things well: plan, automate, and monitor. The Apache Airflow community-built platform for programmatically building, scheduling, and monitoring workflows.
Scalable
Scheduling
User Interface
Notification/Alert System
Plugins, Hooks, Sensors
Ability to integrate with other services (such as cloud services)
Available Rest API endpoint container for external use
Airflow used in many industries:
- Big Data
- Machine learning
- Computer software
- Financial Services
- IT services
- Banking etc.
Features of Apache Airflow
You can use the Apache Airflow feature. If you know Python, you can start deploying to Airflow.
- Open Source: It is Free and open source with many active users.
- Powerful integration: This allows operators to work with Google Cloud Platform, Amazon AWS, Microsoft Azure, and more.
- Use standard Python for coding: create simple and complex workflows with complete flexibility.
- Fantastic user interface: Control and manage your workflow. It allows you to see the status of completed and running jobs.
How is Apache Airflow different?
Below are the differences between Airflow and other workflow management platforms.
Directed Acyclic Graphs (DAGs) are written in Python and have a smoother learning curve than Java with Oozie.
A large community has contributed to his Airflow, making it easy to find integrated solutions from leading services and cloud providers.
Airflow is versatile, expressive, and designed for creating complex workflows. The service provides advanced metrics about your workflow.
Airflow has a rich API and an intuitive user interface compared to other workflow management platforms.
The Jinja template enables use cases like referencing a filename that matches the date of a DAG run.
They have managed Airflow cloud services such as AWS MWAA.
Why Apache Airflow?
This section examines Airflow’s strengths and weaknesses and some notable use cases.
Pros
Open Source: Download Airflow, use it today and collaborate with fellow community members.
- Cloud Integration: Airflow works well in a cloud environment and offers many options.
- Scalable: Airflow is highly scalable up and down. It can be deployed on a single server or scaled to large multi-node deployments.
- Flexible and Customizable: Airflow is designed to work with the standard architecture of most software development environments, but its flexibility allows for many customization options.
- Surveillance: Airflow allows for different types of guidance. For example, you can view task status from the UI.
- Code First Platform: This code dependency allows you to write the code that runs at each pipeline step.
- Community: Airflow’s large and active community helps you expand your knowledge and network with like-minded people.
Cons
- Reliance on Python: Many people think it makes sense that Airflow relies heavily on Python code, but those with little Python experience will find the learning curve can be steep.
- Interference: Airflow is generally reliable, but as with any product, interference can occur.
Use Cases
Airflow can be used for nearly all batch data pipelines, and there are many documented use cases, the most common being Big Data-related projects. Here are some examples of use cases listed in Airflow’s GitHub repository:
- Using Airflow with Google Big Query to power a Data Studio dashboard.
- Using Airflow to help architect and govern a data lake on AWS.
- Using Airflow to tackle the upgrading of production while minimizing downtime.
Installation Steps
Let’s start by installing Apache Airflow. You can skip the first command if you already have pip installed on your system. Installing pip can be done using a terminal by running the following command:
sudo apt-get install python3-pip
Next, Airflow needs a home on the local system. By default, ~/airflow is the default location, but you can change it if you want.
export AIRFLOW_HOME=~/airflow
Install Apache Airflow with pip using the following command:
pip3 install apache-airflow
Airflow requires a database backend to run your workflows and to maintain them. Now, to initialize the database run the following command.
airflow initdb
We already mentioned that Airflow has an excellent user interface. To start the web server, run the following command in your terminal: The default port is 8080. You can change this port if you use it for another purpose.
airflow webserver -p 8080
Start the airflow schedular using the following command in a different terminal. It will run all the time, monitor all your workflows, and trigger them as you have assigned them.
Components of the Apache Airflow
- DAG: This is a directed acyclic graph. It’s a collection of all the tasks you want to do, organized and showing the relationships between the various functions. It is defined in a Python script.
- Web Server: This interface is based on Flask and allows you to monitor DAG status and triggers.
- Metadata database: All task qualities are stored in a database by the Airflow that performs all the workflow read/write.
- Scheduler: As the name suggests, this component is responsible for scheduling DAG execution. Gets and updates the status of tasks in the database.
Conclusion
Airflow is a platform for programmatically creating, scheduling, and monitoring workflows. For example, the first phase of the workflow requires running a C++-based program to perform image analysis and then a Python-based program to transfer that information to S3. Apache Airflow is a powerful scheduler for programmatically creating, scheduling, and monitoring workflows. Airflow is a platform for defining, executing, and monitoring workflows. Designed to be extensible, you can use plugins to interact with the venue and create as many standard external systems and media as you need—the Apache Airflow community-built platform for programmatically building, scheduling, and monitoring workflows. Features of Apache Airflow You can use the Apache Airflow feature. Flexible and Customizable: Airflow is designed to work with the standard architecture of most software development environments, but its flexibility allows for many customization options. By default, ~/airflow is the default location, but you can change it if you want. Export AIRFLOW_HOME=~/airflow Install Apache Airflow with pip using the following command: pip3 install apache-airflow Airflow requires a database backend to run your workflows and to maintain them. It will run all the time, monitor all your workflows, and trigger them as you have assigned them. Airflow scheduler Components of the Apache Airflow DAG: This is a directed acyclic graph.
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