Apache Airflow Training in Nepal

This instructor-led live training, accessible online or onsite, targets advanced participants interested in deploying Apache Airflow in cloud settings, configuring CI/CD pipelines, and establishing resilient monitoring and logging protocols.

By the conclusion of this training, learners will be able to:

• Deploy Apache Airflow within cloud and containerized ecosystems.
• Configure CI/CD pipelines to automate DAG testing and deployment processes.
• Incorporate monitoring and logging tools to enhance overall system reliability.
• Optimize Airflow settings for superior performance and scalability.
• Enforce security best practices for managing workflows and environments.

This instructor-led, live training in Nepal (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 Nepal (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 Nepal (online or onsite) is designed for intermediate-level participants who aim to enhance workflow performance, manage 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 serves as an open-source platform designed for authoring, scheduling, and monitoring workflows. Leveraging Airflow 2.x, organisations can orchestrate data pipelines at scale, integrate seamlessly with cloud services, and ensure robust observability and security within production environments.

This instructor-led live training, available both online and onsite, is tailored for beginner to intermediate-level data and DevOps professionals seeking to utilise Apache Airflow to build, automate, and scale comprehensive end-to-end data pipelines.

Upon completion of this training, participants will be capable of:

• Installing and configuring Apache Airflow across various environments.
• Authoring, scheduling, and monitoring Directed Acyclic Graphs (DAGs) using the TaskFlow API.
• Integrating Airflow with data sources, cloud services, and machine learning workflows.
• Managing ETL pipelines and guaranteeing data reliability.
• Securing and scaling Airflow through the use of executors, queues, and Kubernetes.
• Applying observability standards and best practices for production deployments.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

• To request customised training for this course, please contact us to arrange.