Hello Readers ! How are you all ? In this article I will share overview about Apache Airflow which is best orchestrator for automation and triggering pipelines in modern data stack and why do we need airflow .
Let's dive into the topic Why Apache Airflow ?
In Real world scenarios we build various pipelines for our projects and orchestrate them. Now Airflow comes into picture here and arises few common questions like below for me.
Why should I use Airflow?
What is the difference when using Airflow ?
Why every company nowadays implementing Airflow in their projects?
The main reason is Apache Airflow is an Open Source Batch Orchestration Workflow platform. The main advantage of Airflow is we can able to create the workflows has piece of Python code and also Airflow is Extensible framework allows us to connect with any latest technology and use it. We can able to build data pipelines as DAGs ( Directed Acyclic Graph) which gives us an brief overview of the workflow steps involved in an user friendly web UI which has all data regarding logs, dags, tasks, etc.
Let's consider an example, Suppose a company X is running 2 or 3 pipelines per day if an error encounters in the pipeline we will debug the error easily and re run the pipeline and resolve it. What if we need to run hundreds of data pipelines per day and orchestrate them if an error occurs in meantime it takes longer time to resolve error and to debug it . As the data pipelines increases it looks difficult for us to understand the dependencies in between the workflows involved in hundreds of data pipelines.
Now we can able to improve efficiency easily using Airflow by testing before deploying the python code and debugging it with correct code and reduce time. Workflows can also be stored in version control systems to roll back previous versions. We can validate our own required functionalities for a DAG by writing specific tests. We can also work collaboratively as team with Airflow by simply by creating users.
Airflow has many advantages due to it's Python Framework and they are
Airflow is Dynamic : We can able to create data pipelines dynamically as we configure it in Python Code.
Example : consider a pipeline demo_dag which is DAG consists of 3 tasks.
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from airflow import DAGfrom airflow.operators.bash import BashOperatorfrom datetime import datetime, timedeltadefault_args = { 'start_date': datetime(2023, 1, 1), 'retries': 3, 'retry_delay': timedelta(minutes=5)}with DAG( dag_id='demo_dag', default_args=default_args, schedule_interval='0 0 * * *' # Runs once per day at midnight) as dag: task1 = BashOperator( task_id='task1', bash_command='echo "Task 1"', ) task2 = BashOperator( task_id='task2', bash_command='echo "Task 2"', )task1 >> task2 Airflow is Extensible : We can able to connect with any latest technology as Airflow contains operators it makes adjustment with any environment.
Airflow is Flexible : We can able to create as many tasks in a DAG without any limit and schedule them without any hassle since Airflow is Scalable and also it is build by leveraging Jinja templating engine.
Finally, Airflow has evolved to be best orchestrator in modern data stack since it is open source every body can use this and develop data pipelines effectively and increases the efficiency of pipelines in troubleshooting the errors in pipelines and backfilling in airflow is an amazing helps in re running pipelines without any hassle. If you know coding Airflow is best tool for you to practice and have fun with developing pipelines and interacting with Web UI.
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