Apache Airflow Training in Tashkent

This instructor-led, live training in Tashkent (online or on-site) targets advanced-level participants aiming to deploy Apache Airflow within cloud environments, establish CI/CD pipelines, and implement comprehensive monitoring and logging practices.

Upon completion 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 Tashkent (available online or on-site) is designed for 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:

• Configure Apache Airflow to orchestrate machine learning workflows.
• Automate tasks such as data preprocessing, model training, and validation.
• Integrate Airflow with various machine learning frameworks and tools.
• Deploy machine learning models through automated pipelines.
• Monitor and optimize machine learning workflows in production environments.

This instructor-led, live training in Tashkent (online or onsite) targets advanced-level participants who aim to build custom operators, sensors, and plugins within Apache Airflow, and integrate them with existing data systems to enable sophisticated automation and monitoring.

Upon completing this training, participants will be able to:

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

This live, instructor-led training, conducted in Tashkent (online or onsite), is designed for intermediate participants aiming to optimize workflow performance, handle complex dependencies, and scale Apache Airflow deployments for large-scale datasets and enterprise scenarios.

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

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

Apache Airflow is an open source platform for authoring, scheduling, and monitoring workflows. With Airflow 2.x, organizations can orchestrate data pipelines at scale, integrate with cloud services, and ensure robust observability and security in production environments.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level data and DevOps professionals who wish to use 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 different environments.
• Author, schedule, and monitor DAGs using the TaskFlow API.
• Integrate Airflow with data sources, cloud services, and ML workflows.
• Manage ETL pipelines and ensure data reliability.
• Secure and scale Airflow using executors, queues, and Kubernetes.
• Apply observability and best practices for production deployments.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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