Apache Airflow Course Overview

To ensure a successful learning experience in our Apache Airflow course at Koenig Solutions, please consider the following minimum prerequisites:

• Basic Understanding of Python: Familiarity with Python programming is essential as Airflow is written in Python and you will be writing scripts in Python to create DAGs (Directed Acyclic Graphs).

  
  

• Fundamental Knowledge of Databases: Understanding basic database concepts and SQL will help you configure and interact with the Airflow database.

  
  

• Conceptual Understanding of Linux/Unix Command Line: Basic commands and navigation skills will be useful, especially for installation and setup.

  
  

• Familiarity with Basic Software Development Principles: Knowledge of source control tools like Git, as well as understanding of basic development environments and workflows will be beneficial.

  
  

• Basic Knowledge of Docker (optional but helpful): Since one of the methods of Airflow installation includes Docker, understanding Docker concepts and operations can be advantageous.

  
  

These prerequisites are aimed at equipping you with the necessary background to grasp the course content effectively and maximize your learning outcome. Regardless of your current skill level, our course is designed to guide you through all necessary aspects of Apache Airflow.

Target Audience for Apache Airflow

Introduction to Apache Airflow Course: This course equips participants with skills in managing and automating data workflows using Apache Airflow, catering primarily to IT professionals in data-oriented roles.

Target Audience:

• Data Engineers
• DevOps Engineers
• Software Developers involved in Data-intensive applications
• System Administrators managing data pipelines
• Data Scientists needing workflow automation
• IT Project Managers overseeing data projects
• Cloud Engineers working with data-driven cloud services
• Technical Architects designing data flow strategies

Learning Objectives - What you will Learn in this Apache Airflow?

Introduction to the Course's Learning Outcomes and Concepts Covered

This Apache Airflow course equips students with the skills to design, implement, and manage workflow automation using Airflow, covering installation, core concepts, advanced features, and integration.

Learning Objectives and Outcomes

• Understand the fundamental concepts of workflow management systems and the role of Apache Airflow.
• Install and configure Apache Airflow, including setting up the necessary infrastructure like the web server and scheduler.
• Grasp core Airflow concepts such as Directed Acyclic Graphs (DAGs), Operators, Sensors, and Executors.
• Develop skills in writing DAGs, using operators to define tasks, and setting task dependencies.
• Employ advanced Airflow features like SubDAGs, TaskGroups, branching, conditional logic, and triggering rules.
• Implement hands-on workflow creation, from simple to complex DAGs, including dynamic dependency management and cross-DAG dependencies.
• Monitor, troubleshoot, and optimize Airflow deployments, including configuring logging, handling retries, and scaling for production.
• Apply best practices for high availability, security, and fault-tolerant deployment of Airflow.
• Integrate Airflow with external systems like databases and cloud services to enhance workflow capabilities.
• Compare Apache Airflow with