Master Airflow: Beginner to Advance with Project
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Apache Airflow is an open-source platform used for workflow automation, scheduling, and orchestration of complex data pipelines. As data volumes and complexity continue to grow, the need for efficient and scalable data processing and management is critical. In this comprehensive course, you will learn how to master Apache Airflow, starting from the basics and progressing to advanced concepts.
The course is designed for data engineers, data scientists, python developers, software engineers, and anyone interested in learning how to automate and manage data workflows.
You will learn how to use Apache Airflow to build and manage data pipelines, schedule, and trigger tasks, monitor and troubleshoot workflows, and integrate with various data sources and services.
The course will cover the following topics:
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Introduction to Apache Airflow and workflow management
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Introduction to Docker and Docker Commands
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Installation and configuration of Apache Airflow
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Building and managing workflows with Apache Airflow
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Scheduling and triggering tasks in Apache Airflow
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Operators in Apache Airflow
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Fetching data from Web APIs or HTTP
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File Sensors in Apache Airflow
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Connecting with Azure or AWS
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Using AWS S3 Bucket and Azure Blob Storage to store and retrieve data
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Creating custom operators and sensors
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Handling dependencies and task retries
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Monitoring and troubleshooting workflows
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Integrating with data sources and services
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Scaling and optimizing Apache Airflow for large-scale data processing using Celery Executors
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Securing Dags Connections using Fernet Keys
Throughout the course, you will work on practical exercises and projects to apply the concepts you learn. By the end of the course, you will have a strong understanding of Apache Airflow and the skills to build and manage complex data workflows.
