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Home / Blog / Interview Questions on Data Engineering / Top 35 Apache Airflow Interview Questions
Sharat Chandra is the head of analytics at 360DigiTMG as well as one of the founders and directors of AiSPRY. With more than 17 years of work experience in the IT sector and Worked as a Data scientist for 14+ years across several industry domains, Sharat Chandra has a wide range of expertise in areas like retail, manufacturing, medical care, etc. With over ten years of expertise as the head trainer at 360DigiTMG, Sharat Chandra has been assisting his pupils in making the move to the IT industry simple. Along with the Oncology team, he made a contribution to the field of LSHC, especially to the field of cancer therapy, which was published in the British magazine of Cancer research magazine.
Table of Content
Apache Airflow is an open-source platform used for orchestrating complex computational workflows and data processing pipelines. It's designed to programmatically author, schedule, and monitor workflows with ease.
Airflow helps in managing workflows by allowing data engineers to script complex data pipelines as Directed Acyclic Graphs (DAGs). It provides an intuitive interface to schedule, monitor, and troubleshoot these workflows.
A Directed Acyclic Graph (DAG) in Airflow is an assemblage of all the jobs you wish to do, arranged to show their interdependencies and linkages.
The Airflow Scheduler monitors all tasks and DAGs, then triggers the task instances once their dependencies are complete. It schedules jobs based on time or external triggers.
The Airflow Meta Database is where Airflow stores its metadata. This includes information about the status of tasks, DAGs, variables, connections, and historical data about the workflow execution.
An operator in Airflow represents a single task, or a unit of work, within a DAG. Each operator determines what actually happens in a task.
Hooks in Airflow are interfaces to external platforms and databases, such as MySQL, PostgreSQL, or HTTP services. They are used to manage connections and interact with external systems.
Python scripts in Airflow are used to define the logic of operators, DAGs, and plugins. They are written as standard Python files and allow for extensive customization and control over your workflows.
The Airflow UI is a web-based interface provided by Apache Airflow that allows users to manage and monitor their workflows, view logs, track DAGs' progress, and troubleshoot issues.
Dependencies in Airflow are defined by setting the relationships between tasks using the set_upstream and set_downstream methods, or the >> and << bitwise operators in Python.
The Airflow Executor is responsible for running the tasks within a DAG. There are different types of executors, such as the LocalExecutor, CeleryExecutor, and KubernetesExecutor, each suited for different use cases.
Workflows in Airflow are monitored using the Airflow UI, which provides information about the execution status of tasks, logs, and allows rerunning of tasks in case of failures.
XComs, or "Cross-communications", are a mechanism in Airflow that allows tasks to exchange messages or data. They are stored in Airflow's metadata database and can be used to pass information between tasks within the same DAG.
Airflow Variables are used to store dynamic values that can be accessed and used in DAGs and tasks. They offer a way to avoid hard-coding and to manage configuration settings.
Testing an Airflow DAG involves checking its correctness and behavior. This can be done by running individual tasks using the Airflow CLI, using unit tests to test task logic, and checking DAG structure and dependencies.
A SubDAG is a DAG used as a task in another parent DAG. It's useful for repeating patterns within a DAG and to modularize complex workflows.
Errors and retries in Airflow are handled by setting the retries and retry_delay parameters in task definitions. Airflow will automatically retry a failed task according to these settings.
The CeleryExecutor is used in distributed environments where you need to run tasks on multiple machines. I used it in a project where tasks were resource-intensive and required to be distributed across different nodes to balance the load.
Sensitive information in Airflow can be secured using Airflow Connections for external systems and Airflow Variables for internal configurations, both of which can be encrypted with Fernet keys.
The BranchPythonOperator is a way to run different tasks based on the logic encoded in a Python function. It's used to control the flow of a DAG execution dynamically.
DAGs in Airflow are scheduled by setting the start_date, end_date, and schedule_interval parameters in the DAG definition. These parameters determine when and how often the DAG should run.
Yes, Airflow is commonly used for ETL processes. It orchestrates the extraction, transformation, and loading of data by scheduling and managing the tasks that comprise these processes.
In Airflow, a DAG is a collection of tasks organized with dependencies and relationships to define a workflow. A task, on the other hand, is a single operation or step within a DAG, defined by an operator.
Airflow manages dependencies using task relationships. When a task is set as downstream of another, it will only run once the upstream task has successfully completed.
Airflow Plugins are a way to extend the functionality of Airflow. They allow you to add new operators, hooks, and interfaces to integrate with new systems or perform specific tasks that are not available in the standard Airflow installation.
High availability in Airflow can be achieved by setting up a multi-node cluster with a database like PostgreSQL or MySQL that supports high availability and using a distributed executor like the CeleryExecutor.
A Task Instance in Airflow is a specific run of a task. It represents a task's execution at a particular point in time, with its own logs, state, and context.
Data lineage in Airflow can be managed using XComs to pass metadata between tasks, and by using task and DAG documentation to describe the flow and transformations of data.
Yes, Airflow can be used for non-ETL workflows. It is a versatile tool that can orchestrate any type of task that can be executed in a Python environment, including data analysis, machine learning model training, and more.
Task dependencies from external systems can be handled in Airflow using Sensors. Sensors are a special kind of operator that wait for a certain condition or event to occur in an external system before proceeding.
In a microservices architecture, Airflow can be used to orchestrate the interactions between different services. It can schedule and manage tasks that involve multiple microservices, ensuring the right order of operations and handling failures.
Best practices for scaling Airflow include using a distributed executor like CeleryExecutor, ensuring your database is optimized and can handle the load, splitting your DAGs into smaller, more manageable pieces, and monitoring your Airflow instances to understand the resource usage.
Configuration changes in Airflow can be managed by using Airflow Variables and Connections, which can be set and modified either via the UI or the command line interface.
The Airflow Webserver provides the web UI for Airflow. It allows users to visualize DAGs, monitor task progress, view logs, manage Airflow configuration, and troubleshoot issues.
Automating the deployment of Apache Airflow DAGs (Directed Acyclic Graphs) can be achieved through a combination of version control tools like Git, CI/CD (Continuous Integration/Continuous Deployment) pipelines, and proper configuration of the Airflow environment.
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