Mastering Apache Airflow: Advanced Dags, Scaling, & Hands-On

Master advanced Apache Airflow techniques for automation, performance tuning, and secure workflow orchestration.

What you'll learn

Design and manage complex Apache Airflow workflows by implementing DAGs with advanced scheduling, dependencies, and optimizations.

Enhance data pipeline efficiency using partitioning, sharding, caching, and parallel processing techniques for large-scale datasets.

Optimize and scale Airflow deployments by configuring Celery/Kubernetes executors, improving load balancing, and ensuring high availability.

Secure and monitor Airflow environments by implementing

Requirements

No prior Apache Airflow experience needed – This course covers everything from the basics to advanced topics.

Basic Python knowledge is recommended – Familiarity with Python will help in writing DAGs and custom operators.

Understanding of data pipelines is helpful – Knowledge of ETL processes or workflow automation concepts can be beneficial.

Description

Apache Airflow is the leading open-source tool for orchestrating complex data workflows. This advanced course, "Expert Apache Airflow: Automation, Optimization & Security," is designed for data engineers, DevOps professionals, and workflow architects who want to master Airflow’s advanced capabilities and real-world applications.You will dive deep into DAG optimization, custom operators, XComs, trigger rules, and SLAs to build efficient workflows. Learn how to monitor, scale, and optimize performance using Celery, Kubernetes, and advanced logging techniques. The course also covers integration with AWS, GCP, and Big Data technologies, enabling seamless automation of data pipelines and cloud environments.Security is a key focus, with hands-on lessons in authentication, role-based access control (RBAC), secure connections, and encryption to ensure safe and compliant workflows. Additionally, you will explore troubleshooting strategies, performance tuning, and real-world case studies from industries like finance, e-commerce, and healthcare.Each module includes hands-on projects, ensuring you gain practical experience implementing advanced Apache Airflow features. By the end of the course, you’ll be equipped to build, scale, and secure high-performance workflow automation in any enterprise environment.Who should enroll?Data Engineers & ArchitectsDevOps & Cloud ProfessionalsWorkflow Automation EnthusiastsTake your Apache Airflow expertise to the next level and become an industry-ready workflow automation expert today!

Overview

Section 1: Introduction to Apache Airflow

Lecture 1 What is Apache Airflow?

Lecture 2 History of Apache Airflow

Lecture 3 Installation and setup

Lecture 4 Setting up Apache Airflow Environment Demo

Section 2: DAGs in Apache Airflow

Lecture 5 Understanding Directed Acyclic Graphs (DAGs)

Lecture 6 Operators in Apache Airflow

Lecture 7 Sensors and Executors

Lecture 8 Building and Testing DAGs Demo

Section 3: Advanced Concepts in Apache Airflow

Lecture 9 XComs and Variables

Lecture 10 Custom Operators and Executors

Lecture 11 Data profiling and quality checks

Lecture 12 Trigger rules and SLAs

Lecture 13 Implementing Advanced Features in Apache Airflow Demo

Section 4: Monitoring and Scaling Apache Airflow

Lecture 14 Logging and Monitoring in Apache Airflow

Lecture 15 Best practices for performance optimization

Lecture 16 Error handling and retries

Lecture 17 Monitoring and scaling Apache Airflow Demo

Section 5: Integration with Other Tools

Lecture 18 Integrating Apache Airflow with AWS services

Lecture 19 Integrating Apache Airflow with GCP services

Lecture 20 Integrating Apache Airflow with Big Data technologies

Lecture 21 Using Apache Airflow with Docker and Kubernetes

Lecture 22 Integrating Apache Airflow with external tools Demo

Section 6: Case Studies and Real-World Examples

Lecture 23 Financial data processing with Apache Airflow

Lecture 24 E-commerce data pipeline using Apache Airflow

Lecture 25 Social media analytics with Apache Airflow

Lecture 26 Healthcare data management with Apache Airflow

Lecture 27 Implementing real-world use cases with Apache Airflow Demo

Section 7: Security and Permissions in Apache Airflow

Lecture 28 Authentication and Authorization in Apache Airflow

Lecture 29 Secure connections and data encryption

Lecture 30 Role-based access control in Apache Airflow

Lecture 31 Setting up secure environments

Section 8: Advanced Data Processing Techniques

Lecture 32 Data partitioning and sharding

Lecture 33 Data encryption and decryption

Lecture 34 Data caching and pre-fetching

Lecture 35 Parallel processing in Apache Airflow

Lecture 36 Resolving common issues and optimizing performance Demo

Section 9: Optimizing Performance and Scalability

Lecture 37 Performance monitoring and tuning

Lecture 38 Scaling Apache Airflow for large datasets

Lecture 39 Load balancing and resource optimization

Lecture 40 High availability and disaster recovery strategies

Data Engineers & ETL Developers – Professionals looking to automate and optimize data workflows using Apache Airflow.,Data Scientists & Analysts – Those who want to orchestrate machine learning pipelines and manage large-scale data processing.,Beginners in Data Engineering – Anyone curious about learning workflow orchestration and building expertise in Apache Airflow, even with no prior experience.