Apache Airflow Training in Plovdiv

This instructor-led, live training in Plovdiv (online or onsite) is intended for advanced participants seeking to deploy Apache Airflow in cloud environments, set up CI/CD pipelines, and implement reliable 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, delivered Plovdiv (online or onsite), is designed for intermediate-level participants who wish to automate and manage machine learning workflows. The curriculum covers model training, validation, and deployment using Apache Airflow.

Upon completion of this training, participants will be equipped to:

• Configure Apache Airflow specifically for orchestrating machine learning workflows.
• Automate essential tasks such as data preprocessing, model training, and validation.
• Seamlessly integrate Airflow with various machine learning frameworks and tools.
• Deploy machine learning models through the use of automated pipelines.
• Monitor and optimize machine learning workflows within production environments.

This live, instructor-led training in Plovdiv (online or onsite) is designed for 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 Plovdiv (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 authoring, scheduling, and monitoring workflows. With the release of Airflow 2.x, organizations can orchestrate large-scale data pipelines, integrate seamlessly with cloud services, and ensure robust observability and security within production environments.

This instructor-led live training (available online or onsite) is tailored for beginner to intermediate data and DevOps professionals who aim to utilize Apache Airflow to build, automate, and scale comprehensive data pipelines.

Upon completion of this training, participants will be able to:

• Install and configure Apache Airflow across various environments.
• Author, 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 and ensure data reliability.
• Secure and scale Airflow using executors, queues, and Kubernetes.
• Implement observability measures and best practices for production deployments.

Format of the Course

• Interactive lecture and discussion.
• Extensive 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.