Data Pipeline Automation with Airflow

Data Pipeline Automation with Airflow equips participants with the essential skills to design, implement, and manage automated data workflows using Apache Airflow. This course emphasizes hands-on, project-based learning, allowing participants to engage with real-world scenarios that enhance their understanding of data engineering principles. By the end of the program, learners will have the capability to construct data pipelines that are efficient, scalable, and maintainable, positioning them for success in the evolving landscape of data analytics.
The curriculum is structured to provide a comprehensive exploration of Airflow’s functionalities, including task scheduling, workflow management, and integration with various data sources. Participants will work on a final project that involves creating a fully automated data pipeline, which they can showcase in Cademix Magazine. This practical approach not only solidifies the theoretical concepts but also enhances the learner's portfolio, making them more competitive in the job market.
Introduction to Data Pipelines and Workflow Automation
Overview of Apache Airflow Architecture and Components
Setting Up Airflow: Installation and Configuration
Creating Directed Acyclic Graphs (DAGs) in Airflow
Task Management: Operators, Sensors, and Hooks
Scheduling and Monitoring Workflows
Integrating Airflow with Cloud Services and Databases
Error Handling and Debugging in Data Pipelines
Best Practices for Data Pipeline Design
Final Project: Building a Complete Data Pipeline with Airflow

To develop proficiency in designing and automating data workflows using Apache Airflow.

Graduates, job seekers, business professionals, and optionally researchers or consultants.

Basic understanding of Python programming and familiarity with data engineering concepts.