Discuss the cases that Apache Airflow is not used for and this will help you to know more about Apache airflow capabilities
Apache Airflow has gained popularity as a powerful workflow management tool that enables users to schedule, manage, and monitor data pipelines (As explained here). However, it is important to understand that Airflow is not a data streaming or processing tool, and it is not designed to handle large volumes of data. In this article, we will explore these limitations in more detail and explain why it is important to use specialized tools for data streaming and processing.
Airflow is not a Data Streaming Tool
One of the most common misconceptions about Apache Airflow is that it is a data streaming tool. While Airflow can be used to schedule and manage data pipelines, it is not designed to handle real-time data streams. Airflow is a batch processing tool that operates on data that has already been collected and stored in a database or file system.
If you need to work with real-time data streams, other tools are better suited for this task, such as Apache Kafka or Apache Flink. These tools are designed to handle massive volumes of data in real-time and provide low-latency processing capabilities that Airflow simply cannot match.
Airflow is not a Data Processing Tool
Another common misconception is that Apache Airflow is a data processing tool. While Airflow can execute Python code and perform tasks that manipulate data, it is not designed to handle large volumes of data processing. It is recommended to avoid processing a large amount of data within Airflow DAGs (Directed Acyclic Graphs).
The reason for this is that Airflow is built to orchestrate workflows, not to perform intensive data processing. When you try to perform data processing within Airflow, you may encounter performance issues and scalability problems. Instead, it is recommended to use specialized tools for data processing, such as Apache Spark or Apache Beam, and then integrate them with Airflow to manage the workflow.
