Big Data Orchestration Using Apache Airflow

Components of Apache Airflow

In this section we will discuss the components of the Apache Airflow system that help in creating and managing the data pipelines to manage the workflow.

Scheduler

The Scheduler is responsible for managing the execution and scheduling of the tasks. The scheduler analyzes all the dependencies and the time conditions and decides which task will be triggered first.

DAG

DAG stands for Directed Acyclic Graph. DAG represents the overall structure and how various tasks are interdependent on each other. DAGs also help in visualizing the workflow of the process.

Operators

Operators are used to perform particular tasks in the workflow. Airflow has multiple operators like BashOperator, SQLOperator, PythonOperator etc. which perform specific operations.

Executors

Executors manage the execution of the tasks in the Airflow. Some examples of executors include LocalExecutor, CeleryExecutor and KubernetesExecutor.

Plugins

Airflow also has plugins that allow users to add custom operators and hooks to meet their requirements.

Steps to Install Apache Airflow

In this section, we will see all the steps involved in installing Apache Airflow on your system.

Since Apache Airflow uses python, your system must have Python and pip installed. The rest of the steps in installing Apache Spark is quite easy.

Step1: Installing the dependencies

The first step in setting up Airflow on your device is to install the required dependencies.

sudo pacman -S python-pip python-setuptools python-wheel
Sudo pacman -S postgresql - libs libffi

Step2: Installing Airflow using pip

The next step in setting up airflow is to install Airflow using pip.

pip install apache-airflow

This command will install the airflow package on your device.

Step3: Initialize the Airflow database

Now we need to initialize our airflow database using the following command.

Airflow db init

Step4: Creating the configuration file

We can create our Airflow Configuration file using the command given below.

Airflow config create-airflow-config

Step5: Starting the Airflow Server and Scheduler

The last step remaining in setting up Apache Airflow is starting the Airflow Webserver and the Scheduler.

airflow webserver –port 8080
airflow scheduler

After completing all these steps Apache Airflow should be installed on your device and you can access it using the port specified by you.

How to Create Workflow using Apache Airflow?

In this section, we will cover the steps to create workflows using Apache Airflow.

Step1: Creating the DAG

The first step in creating any workflow is to create the activity DAG. For this, we first need to import the DAG class from airflow.

from airflow import DAG

sample_dag= DAG(
	'Sample_dag',
	description = 'This is a sample DAG'
	schedule_interval ='@daily',
	start_date =datetime(2023, 20, 7)
)

Step2: Creating functions

After creating our DAG, we can define our workflow functions. A sample function that can be used in our DAG is shown below.

def my_func():
	print("This is a temporary function")

Step3: Attach the function to the operators

After creating the function, we need to create new operators and attach the newly created function to the operators.

from Airflow.operators.bash_operator import BashOperator

task1 = BashOperator(
	id='id1'
	bash_command='echo "This is the first task" ',
	dag=sample_dag
)

Step4: Define the order of execution

As the last step, we need to define the order in which the operators will be executed. This is achieved by setting the dependencies between the tasks.

task1 >> task2
task1 >> task2
task2 >> task3
task3 >> task4

Advantages and Disadvantages of Apache Airflow

Now that we have discussed almost everything about Apache Airflow in detail, we shall now discuss the advantages and disadvantages of performing Big data Orchestration using Apache Airflow. These are the following advantages of Apache Airflow.

Advantages

• Scalable
   
• Easy to Use
   
• Easy Integration with other Tools
   
• Good Community Support
   
• Good Fault Tolerance

 

The following are the disadvantages of using Apache Airflow.

Disadvantages

• Difficult for Beginners to use
   
• Minimal Built-in Data Processing Capabilities
   
• High Maintenance Cost
   
• High resource Usage

Frequently Asked Questions

Is it possible to use Apache Airflow for real-time processing?

Apache Airflow is mainly designed for offline data processing but it can also be used for real-time processing upto some limits. Apache Airflow provides support for external triggering to execute tasks.

How does Apache Airflow allow us to visualize our workflows?

Apache Airflow provides us a web UI (User Interface) called Airflow UI. This UI helps us to visualize our workflows, enables us to view the graphical form of DAGs and also visualize our task status and execution logs.

How does Apache Airflow allow parallel processing?

Apache Airflow allows the user to distribute data into several different partitions using various schemes supported by it. These partitions can be run simultaneously and allow efficient processing of large datasets.

Conclusion

In this article, we discussed Big Data Orchestration Using Apache Airflow. We discussed the components of Apache Airflow along with its installation process. In the end, we concluded by discussing some advantages and disadvantages of using Apache Airflow and some frequently asked questions.

So now that you know what Big Data Orchestration using Apache Airflow is, you can refer to similar articles.
 

• Introduction to Apache Spark
• Aggregation in DBMS
• Amazon Managed Streaming for Apache Kafka
• Apache vs NGINX
• multiple granularity in dbms
   

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