Introduction to Apache Airflow: Workflow Automation with DAGs and Tasks
This short course on Cloud/Edge Computing for Deep Learning and Big Data Analytics provides a comprehensive overview and detailed presentation of advanced technologies utilized in distributed computational systems. Distributed computing plays a critical role in today’s data-driven landscape, enabling the processing of vast amounts of data efficiently and effectively across multiple nodes and locations. Through the distribution of computing tasks across systems, distributed computing amplifies scalability, reliability, and performance, proving indispensable for managing big data analytics, machine learning, and real-time processing tasks.
In today’s technological landscape, process automation has become a fundamental component for improving the efficiency and productivity of organizations. In this introductory conference on Apache Airflow, we will explore how to use DAGs (Directed Acyclic Graphs) together with tasks to orchestrate and automate workflows effectively.
During the session, we will delve into the concept of DAGs and how to use them to define workflows, organizing tasks into a logical and sequential flow. We will also explore the role of tasks within DAGs, explaining how they represent individual units of work and how they can be configured to perform specific actions, such as data processing, script execution, or notification sending.
We will also explore how Airflow simplifies the programming and execution of tasks, offering a wide range of predefined operators and the flexibility to create custom operators to meet the specific needs of the workflow. We will demonstrate how to define tasks within a DAG, schedule them, and monitor them to ensure reliable and smooth execution.