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Apache Airflow is an open-source platform that allows you to programmatically author, schedule, and monitor workflows. It is extremely easy to use and anybody with Python knowledge can seamlessly deploy a workflow.
It allows you to use date time formats for scheduling and loops feature to dynamically generate tasks. One of the best features is its UI. It enables you to monitor and manage your workflows and provides a 360-degree view of completed and ongoing tasks.
Another strong feature of Apache Airflow is its ability to easily integrate with leading platforms such as Google Cloud Platform, Databricks SQL, Redis, Singularity, Amazon Web Services, Microsoft Azure, and other third-party services.
Airflow Principles
Scalable Architecture
Provides a modular framework that scales based on your requirements
Code-Driven
Pipelines written in Python & workflows can be programmatically constructed to tackle complex tasks
Customizable
Allows you to create custom operators and extend libraries to suit your environment
Easy to Read & Manage
Natively supports parameterization through the Jinja templating engine
What is Apache Airflow used for?
You can use Apache Airflow for different purposes. Companies all over the world use Apache Airflow for use cases like business operations, ETL/ELT, infrastructure management, and MLOps. Let’s pick each use case one by one.
Apache Airflow is the perfect option for orchestrating business operations. It helps organizations get data that can power their business applications. Be it aggregating user data, preparing data for large language models, or presenting detailed analytics on dashboards – Airflow does it all.
Airflow provides you with built-in features like automatic retries, complex dependencies, & branching logic that make it easier to orchestrate MLOps pipelines. You get a production-ready monitoring & alerting modules like Airflow notifiers, extensive logging features that give you full control over how you monitor your machine learning operations.
When it comes to infrastructure management, Apache Airflow offers the setup/teardown tasks feature that you can use to manage the infrastructure required to run other tasks. Another relevant use case here is Airflow’s ability to perform data quality checks.
With Apache Airflow, you can schedule your direct acyclic graph in a data-driven manner. It also streamlines your interactions with object storage systems such as Amazon S3, Google Cloud Storage, and Azure Blob Storage.
Is Apache Airflow an ETL tool?
Yes, it is considered as an Extract-Transform-Load tool. It helps you with building ETL pipelines and managing ETL processes. In fact, it’s said that Extract-Transform-Load data pipelines is one of the most common use cases of Apache Airflow. Airflow can be used to orchestrate ELT pipelines for any data source or destination.
What is the difference between Apache Airflow and Kafka?
Both Apache Airflow and Kafka are open-source platforms and are used in data engineering and data processing tasks. However, they serve different purposes. Kafka runs in a distributed streaming environment and helps you with building real-time data pipelines and streaming applications. On the other hand, Airflow helps you with workflow management – schedule and orchestrate complex workflows.
