Are you trying to prepare for an Airflow interview? If yes, you’d be in search of the latest data and material, right? This post will help you find the latest Airflow interview questions for both beginners and professionals.
Automation of tasks plays a crucial role in almost every industry. Moreover, it’s one of the instant methods to accomplish functional efficiency. However, a lot of us simply fail to comprehend how tasks can be automated. So, in the end, they get tangled in a loop of manual labor, doing the same thing time and again.
This becomes even more difficult for those professionals who deal with a variety of workflows, such as accumulating data from several databases, preprocessing the data, and uploading and reporting the same.
Apache Airflow is a tool that turns out to be helpful in this situation. Whether you’re a software engineer, data engineer or data scientist, this tool is useful for everybody. So, if you’re trying to look for a job in this domain, this post covers some of the latest Airflow interview questions for beginners and professionals to go through.
Airflow Interview Questions and Answers 2024 (Updated) have been divided into two stages they are:
Are you a beginner in the field of Airflow, and you’ve just started giving interviews now? If yes, these Airflow interview questions for beginners will be helpful to a great extent.
Apache Airflow is referred to as an open-source platform that is used for workflow management. This one is a data transformation pipeline Extract, Transform, Load (ETL) workflow orchestration tool. It initiated its operations back in October 2014 at Airbnb. At that time, it offered a solution to manage the increasingly complicated workflows of a company. This Airflow tool allowed them to programmatically write, schedule and regulate the workflows through an inbuilt Airflow user interface.
Some of the issues and problems resolved by Airflow include:
Some of the features of Apache Airflow include:
Airflow solves a variety of problems, such as:
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Airflow has four basic concepts, such as:
Some of the integrations that you’ll find in Airflow include:
The command line is used to run Apache Airflow. There are some significant commands that everybody should know, such as:
There are two different methods to create a new DAG, such as:
Cross Communication (XComs) is a mechanism that allows tasks to talk to one another. The default tasks get isolated and can run on varying machines. They can be comprehended by a Key and by dag_id and task_id.
Jinja templates assist by offering pipeline authors that contain a specific set of inbuilt Macros and Parameters. Normally, it’s a template that contains Expressions and Variables.
If you’ve been a professional in the Airflow domain and are thinking of switching your job, these Airflow interview questions for professionals will be useful during the preparation.
To design workflow in this tool, a Directed Acyclic Graph (DAG) is used. When creating a workflow, you must contemplate how it could be divided into varying tasks that can be independent. And then, the tasks are combined into a graph to create a logical whole.
The overall, comprehensive logic of the workflow is dependent on the graph’s shape. An Airflow DAG can come with multiple branches, and you can select the ones to follow and the ones to skip during the execution of the workflow.
Also:
There are four primary Airflow components, such as:
The Executors, as mentioned above, are such components that execute tasks. Thus, Airflow has a variety of them, such as:
Here are the pros and cons of Executors in Airflow.
Executors | Pros | Cons |
SequentialExecutor |
|
|
LocalExecutor |
|
|
CeleryExecutor |
|
|
KubernetesExecutor |
|
|
To define workflows in Airflow, Python files are used. The DAG Python class lets you create a Directed Acyclic Graph, which represents the workflow.
from Airflow.models import DAG
from airflow.utils.dates import days_ago
args = {
'start_date': days_ago(0),
}
dag = DAG(
dag_id='bash_operator_example',
default_args=args,
schedule_interval='* * * * *',
)
You can use the beginning date to launch any task on a certain date. The schedule interval also specifies how often every workflow is scheduled to run. Also, ‘* * * * *’ represents that the tasks should run each minute.
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Some of the dependencies in Airflow are mentioned below:
freetds-bin \
krb5-user \
ldap-utils \
libffi6 \
libsasl2-2 \
libsasl2-modules \
locales \
lsb-release \
sasl2-bin \
sqlite3 \
The Airflow web server can be restarted through data pipelines. Also, the backend process can be started through this command:
airflow webserver -p 8080 -B true
The bash script file can run with this command:
create_command = """
./scripts/create_file.sh
"""
t1 = BashOperator(
task_id= 'create_file',
bash_command=create_command,
dag=dag
)
We can add logs either through the logging module or by using the below-mentioned command:
import
dag = xx
def print_params_fn(**KKA):
import logging
logging.info(KKA)
return None
print_params = PythonOperator(task_id="print_params",
python_callable=print_params_fn,
provide_context=True,
dag=dag)
We can use Airflow XComs in Jinja templates through this command:
SELECT * FROM {{ task_instance.xcom_pull(task_ids='foo', key='Table_Name') }}
Once you’re backed up by the right type of preparation material, cracking an interview becomes a seamless experience. So, without further ado, refer to these Airflow interview questions mentioned above and sharpen your skills substantially.
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