Apache Airflow: Custom Task Triggering for Efficient Data Pipelines

Apache Airflow is an indispensable tool for orchestrating data pipelines, making it a must-know tool for any data engineer in 2023. Like any tool, Airflow has its advantages and disadvantages. While it boasts excellent built-in functionality, there are situations where custom solutions are required to address specific use cases. One of the captivating aspects of Airflow is its high level of customizability, making it a fascinating tool for data engineers like myself. To fully comprehend the concepts and solutions presented in this article, readers are expected to have a foundational understanding of Airflow. These include familiarity with DAGs, knowledge of tasks and operators, understanding of trigger rules for task execution and basic Python programming skills. While you don’t need to be an Airflow expert, having these basics in place will enable you to follow along and explore the customization magic that Airflow has to offer. For those new to the world of data engineering and Apache Airflow, imagine Directed Acyclic Graphs (DAGs) as a visual representation of your data pipeline’s workflow. Picture a flowchart where each box represents a task, and arrows illustrate the order in which tasks should be executed. Importantly, DAGs don’t allow cycles, meaning the tasks can’t loop back onto themselves or create circular dependencies. This ensures a clear path for data to move through the pipeline without confusion. <a href="https://medium.com/gbtech/apache-airflow-custom-task-triggering-for-efficient-data-pipelines-7fd6f563129e">Read More</a>