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Apache Airflow Masterclass: Build Real-World Projects
Udemy MOOC / Non-credit 0

Apache Airflow Masterclass: Build Real-World Projects

About this course

Apache Airflow is one of the most in-demand tools in modern data engineering, analytics engineering, and backend data platforms. This course is a complete, production-focused Apache Airflow masterclass, designed to take you from absolute beginner to confident, real-world Airflow user.This is not a short tutorial or a surface-level walkthrough. You will first learn Apache Airflow A-to-Z, understanding how it actually works under the hood, and then you will apply that knowledge by building a full end-to-end data engineering project using real-world data workflows.Part 1: Apache Airflow Full Course (Beginner to Advanced)In the first part of the course, we start from scratch and gradually move to advanced and production-ready concepts. Each lecture is clearly structured and mapped to real-world usage.You will learn:What Apache Airflow is, why it is used, and when it should or should not be usedCore building blocks: DAGs, Tasks, Operators, Hooks, Sensors, and XComComplete Airflow architecture explained visually, including Scheduler, Webserver, Executor, Workers, and Metadata DatabaseDifferent Airflow executors (Local, Celery, Kubernetes) and how to choose the right oneInstalling and running Apache Airflow using Docker and local setupsNavigating and using the Airflow UI effectivelyWriting DAGs using the modern TaskFlow APIDeep dive into operators with real demos (Python, Bash, Cloud, and Sensors)Variables, Connections, Secrets, and configuration best practicesXCom internals, common mistakes, and anti-patternsScheduling concepts including cron syntax, timetables, catchup, and backfillTask Groups and Dynamic Task Mapping with hands-on examplesError handling, retries, logging, monitoring, and production best practicesBy the end of this section, you will u

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What you'll learn

  • Understand the fundamentals of Apache Airflow, including its purpose and applications
  • Navigate the Airflow UI and deploy Airflow using different executors
  • Create and manage Directed Acyclic Graphs (DAGs) for workflow orchestration
  • Implement error handling and monitoring best practices for production environments

Course objectives

  • Develop a strong foundational knowledge of Apache Airflow
  • Gain practical experience with real-world data workflows
  • Learn to configure and customize Airflow for specific use cases
Cloud Computing #python #cloud computing #variables #airflow #docker #data engineering #monitoring #logging #bash #task management #data workflows #schedulers #production best practices #dag #executors #connections
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