As a result, integrating next-generation technologies into existing infrastructure and scaling up is simple. High Extensibility with Robust Integrations: Airflow offers many operators to operate on Google Cloud Platform, Amazon Web Services, and a variety of other third-party platforms.It’s a general-purpose orchestration framework with a user-friendly set of features. Scalable: Airflow is a modular solution that orchestrates an arbitrary number of workers via a message queue.Furthermore, owing to the advanced scheduling semantics, users can run pipelines at regular intervals. It’s built on the advanced Jinja template engine, which allows you to parameterize your scripts. Robust Pipelines: Airflow pipelines are simple and robust.It also enables users to pick up where they left off without having to restart the entire operation. Users can develop Machine Learning models, manage infrastructure, and send data with no restrictions on pipeline scope. Easy to Use: An Airflow Data Pipeline can be readily set up by anybody familiar with Python programming language.Let’s have a look at some of the outstanding features that set Airflow apart from its competitors: Since it is distributed, scalable, and flexible, it is ideal for orchestrating complicated Business Logic. ![]() It connects to a variety of data sources and can send notifications to users through email or Slack when a process is completed or fails. Visualizing pipelines in production, monitoring progress, and resolving issues is a snap with Airflow’s robust User Interface. Image SourceĪirflow allows users to create workflows as DAGs (Directed Acyclic Graphs) of tasks. Airflow has evolved into one of the most powerful open source Data Pipeline systems currently offered in the market. Your Data Pipelines can all be monitored in real-time. It’s one of the most trusted solutions for orchestrating operations or Pipelines among Data Engineers.
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