Apache Airflow Training in Taiwan

This instructor-led, live training in Taiwan (online or onsite) is aimed at advanced-level participants who wish to deploy Apache Airflow in cloud environments, set up CI/CD pipelines, and implement robust monitoring and logging practices.

By the end of this training, participants will be able to:

• Deploy Apache Airflow in cloud and containerized environments.
• Set up CI/CD pipelines to automate DAG testing and deployment.
• Integrate monitoring and logging tools to enhance system reliability.
• Optimize Airflow configurations for performance and scalability.
• Implement security best practices for managing workflows and environments.

This instructor-led, live training in Taiwan (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

By the end of this training, participants will be able to:

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led, live training in Taiwan (online or onsite) is aimed at advanced-level participants who wish to create custom operators, sensors, and plugins in Apache Airflow, and integrate these with existing data systems for advanced automation and monitoring.

By the end of this training, participants will be able to:

• Develop custom operators, hooks, and sensors tailored to specific workflows.
• Design and implement Airflow plugins to extend functionality.
• Integrate Airflow workflows with external systems and services.
• Optimize and monitor workflows using advanced debugging tools.
• Leverage best practices for managing large-scale Airflow deployments.

This instructor-led, live training in Taiwan (online or onsite) is aimed at intermediate-level participants who wish to optimize workflow performance, handle complex dependencies, and scale Apache Airflow deployments for larger datasets and enterprise use cases.

By the end of this training, participants will be able to:

• Optimize Apache Airflow workflows for better performance and reliability.
• Manage and troubleshoot complex workflow dependencies.
• Leverage advanced Airflow features, including custom operators and sensors.
• Scale Apache Airflow for handling larger data sets and distributed systems.
• Implement best practices for monitoring, logging, and security in Airflow environments.

Apache Airflow is an open-source platform designed for creating, scheduling, and monitoring workflows. With the latest 2.x version, organizations can manage data pipelines at scale, integrate with cloud services, and ensure robust observability and security in production environments.

This instructor-led, live training (available online or on-site) is tailored for beginner to intermediate-level data and DevOps professionals who want to leverage Apache Airflow to build, automate, and scale end-to-end data pipelines.

By the end of this training, participants will be able to:

• Install and configure Apache Airflow in various environments.
• Create, schedule, and monitor Directed Acyclic Graphs (DAGs) using the TaskFlow API.
• Integrate Airflow with data sources, cloud services, and machine learning workflows.
• Manage ETL pipelines to ensure data reliability.
• Secure and scale Airflow by utilizing executors, queues, and Kubernetes.
• Apply best practices for observability in production deployments.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Course Customization Options

• For customized training options for this course, please contact us to arrange.