The Apache Airflow Data Engineering Training by Multisoft Virtual Academy is a meticulously crafted course designed for aspirants aiming to master workflow orchestration. This immersive training introduces participants to the core concepts of Apache Airflow, emphasizing DAG creation, operator utilization, task templates, and advanced features.

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Apache Airflow Data
Engineering Training
Certification

2. info@multisoftvirtualacademy.com www.multisoftvirtualacademy.com (+91) 8130-666-206
About Multisoft
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About Course
The Apache Airflow Data Engineering Training by Multisoft Virtual Academy is a
meticulously crafted course designed for aspirants aiming to master workflow
orchestration. This immersive training introduces participants to the core concepts of
Apache Airflow, emphasizing DAG creation, operator utilization, task templates, and
advanced features.

3. info@multisoftvirtualacademy.com www.multisoftvirtualacademy.com (+91) 8130-666-206
Module 1: Introduction to Apache Airflow
 What is Apache Airflow?
 Airflow vs. other workflow management systems
 Key concepts: DAG, operators, tasks, etc.
Module 2: Airflow Installation & Setup
 Prerequisites & installation steps
 Setting up the Airflow metadata database
 Running the Airflow web server and scheduler
Module 3: Understanding DAGs (Directed Acyclic Graphs)
 Basics of DAGs
 Creating simple DAGs
 Best practices in DAG design
Module 4: Working with Operators
 Basic, transfer, and database operators
 Using PythonOperator and BashOperator
 Custom operators: creation and use
Module 5: Advanced Task Features
 Task dependencies and relationships
 Dynamic task generation
 Retries and failure handling

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Module 6: Task Templates & Jinja Templating
 Templated commands
 Using macros in Airflow
 Passing variables to templates
Module 7: Monitoring & Logging
 The Airflow web interface: overview and features
 Task logs and metadata
 Troubleshooting failed tasks
Module 8: Parallel and Distributed Execution
 Understanding Airflow executors: Sequential, Local, Celery, Kubernetes
 Scaling Airflow with distributed execution
 Monitoring worker nodes
Module 9: Advanced Features & Plugins
 Creating custom plugins
 Extending Airflow's capabilities
 Integrating with external systems
Module 10: Security & Authentication
 Configuring user authentication
 Role-based access
 Secure execution of DAGs

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Module 11: Best Practices & Use Cases
 Design patterns in Airflow
 Tips for maintainable and scalable DAGs
 Real-world data engineering use cases
Module 12: Moving Forward with Airflow
 Keeping up with the Airflow community
 Future trends and updates
 Transitioning to a production environment
TARGET AUDIENCE
 Data Engineers
 Data Scientists
 Workflow Developers
 ETL Developers
 Big Data Professionals
 DevOps Engineers
 Data Analysts
PRE-REQUISITES
 Basic understanding of programming (preferably Python).
 Familiarity with data processing concepts.
 Knowledge of database management systems.
 Basic understanding of SQL.
 Experience with command-line interfaces.
 Awareness of cloud platforms (like AWS, GCP, Azure) is advantageous but not mandatory.