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The ultimate guide to Apache Airflow

What is Apache Airflow?

Apache Airflow is a tool to visually create, organize, and monitor workflows and launch task chains (pipelines) to process, store and visualize data. Apache Software Foundation owns the platform that still goes through the incubator stage with 1000 contributors on GitHub and 13 000 stars.

Apache Airflow introduction

Apache Airflow is a robust, open-source, Python-written service used by Data Engineers to orchestrate workflows and pipelines by highlighting pipelines’ dependencies, code, logs, trigger tasks, progress, and success status to troubleshoot problems when needed. 

If the task completes or fails, this flexible, scalable, and compatible with external data solution is capable of sending alerts and messages via Slack or email. Apache does not impose restrictions on how the workflow should look and has a user-friendly interface to track and rerun jobs.

How does Apache Airflow work?

Pipelines are described with the use of core elements in Apache:

DAG

The cornerstone of the technology is described by directed acyclic graphs (DAGs). This model is a graph that does not have cycles but has parallel paths coming from the same batch. In simple words, DAG is an entity that combines tasks depending on the data pipeline where the dependence between applications is clearly manifested.
Directed Acyclic Graph (DAG)
Directed Acyclic Graph (DAG)

Task E is the final job in DAG that depends on the successful accomplishment of the preceding tasks to the left.

Operator

An operator is a separate element in the task chain (pipeline). Using these elements, developers describe what task needs to be executed. Apache Airflow has a list of predefined operators that include:

  • PythonOperator performs Python code
  • BashOperator executes bash scripts/commands
  • PostgresOperator calls SQL queries in PostgreSQL
  • RedshiftToS3Transfer runs UNLOAD commands from Redshift to S3
  • EmailOperator sends emails

Tasks and operators are used interchangeably sometimes, but we presume they are different concepts where operators serve as patterns for generating tasks.

Sensor

The sensor is a variation of an operator that finds implementation in event-driven pipelines. Examples:

  • PythonSensor waits for the function to return True
  • S3Sensor checks the availability of the object by the key in the S3 bucket

Hook

Hooks are third-party services that interact with external platforms (databases and API resources). Hooks should not possess vulnerable information to prevent data leakage.

Scheduler

It monitors all the DAGs, handles workflows, and submits jobs to Executor.

Web server

The web server plays the role of Apache Airflow user interface. It helps track the tasks’ status and progress and log data from remote depositaries.

Database

All the pertinent information is stored there (tasks, schedule periods, statistics from each sprint, etc.).

Executor

The Executor runs tasks and pushes them to workers.

Finally, let’s demonstrate how Apache works on a simple example. Firstly, Apache revises all the DAGs in the background. Urgent tasks that need to be completed get the mark SCHEDULED in the database. The Scheduler retrieves tasks from the database and distributes them to Executors. After that, the tasks receive QUEUED status, and once workers start executing them, RUNNING status to the job is assigned. When the task is completed, the worker indicates it as finished/failed depending on the final result’s success, and the Scheduler updates the status in the database.

Apache Airflow Architecture
Apache Airflow Architecture

Apache Airflow features

Below, we list the most exciting features of Apache Airflow.

Easy to operate

Basic Python knowledge is the only requirement to build solutions on the platform.

Open source

The service is free, with many active users worldwide.

Easy integration

One can seamlessly work with complementary products from Microsoft Azure, Google Cloud Platform, Amazon AWS, etc.

Friendly user interface

You can track the status of scheduled and ongoing tasks in real-time.

Apache Airflow Principles

Learn about the basic Apache Airflow principles below.

Dynamic

Airflow pipelines are configured as Python code to make pipelines’ generation dynamic.

Extensible

Users can create defined operators, executors, and libraries suitable for their specific business environment.

Scalable

The service does not crash since it has a modular architecture and can be scaled to infinity.

What are the benefits of Apache Airflow?

They include automation, community, visualization of business processes, as well as proper monitoring and control. We will briefly go through all of them.

Community

There are more than 1000 contributors to the open-source service. They regularly participate in its upgrade.

Business processes’ visualization

Apache is a perfect tool to generate a “bigger picture” of one’s workflow management system.

Automation

Automation makes Data Engineers’ jobs smoother and enhances the overall performance.

Monitoring and control

The built-in alerts and notifications system allows setting responsibilities and implementing corrections.

Apache Airflow

Apache Airflow use cases

The practical effectiveness of the service can be shown in the following use cases:

  • Batch jobs;
  • Scheduling and orchestrating data pipelines workflows with Airflow for a specific time interval;
  • ETL/ELT pipelines that work on batch data;
  • Pipelines that receive data from external sources or conduct data transformation;
  • Apache Airflow for machine learning training models and triggering jobs in SageMaker;
  • Generating reports;
  • Backups from DevOps jobs and saving the results into a Hadoop cluster after executing a Spark job.

 

Apache Airflow as a Service

Plenty of data engineering platforms empowered by Airflow utilize the basic logic and benefits of the service and add new features to solve specific challenges. They can be called Apache Airflow alternatives since they have pretty similar functionality:

  • Astro – a data orchestration platform to create, run, and observe pipelines.
  • Google Cloud Composer – a data orchestration platform to build, schedule, and control pipelines.
  • Qubole – an open data lake platform for machine learning, streaming, and ad-hoc analytics.

Amazon Managed Workflows for Apache Airflow – a managed Airflow workflow orchestration service to set up and operate data pipelines on Amazon Web Services (AWS).

Conclusion

Apache is a powerful tool for data engineering compatible with third-party services and platforms. Migration to Airflow is smooth and trouble-free regardless of the size and specifications of the business.

Innowise Group delivers profound Apache expertise of any complexity and scope. Apache Airflow is a perfect choice to bring order if a client suffers from poor communication between departments and searches for more transparency in workflows.

Our skilled developers will implement a highly customized modular system that improves operation with big data and makes Airflow processes fully managed and adaptable to your business environment’s peculiarities.

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