Apache Airflow Training in St. Louis

This instructor-led, live training in St. Louis (online or onsite) is designed for advanced-level participants who wish to deploy Apache Airflow in cloud environments, establish CI/CD pipelines, and implement robust monitoring and logging practices for government use cases.

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

• Deploy Apache Airflow in cloud and containerized environments for government operations.
• Set up CI/CD pipelines to automate DAG testing and deployment for enhanced efficiency.
• Integrate monitoring and logging tools to improve system reliability and compliance.
• Optimize Airflow configurations for performance and scalability to meet the needs of public sector workflows.
• Implement security best practices for managing workflows and environments, ensuring governance and accountability.

This instructor-led, live training in St. Louis (online or onsite) is designed for intermediate-level participants who aim 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 for orchestrating machine learning workflows.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Apache Airflow with various machine learning frameworks and tools.
• Deploy machine learning models through automated pipelines.
• Monitor and optimize machine learning workflows in a production environment for government.

This instructor-led, live training in St. Louis (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 enhanced automation and monitoring for government.

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

• Develop custom operators, hooks, and sensors tailored to specific workflows for government.
• Design and implement Airflow plugins to extend functionality in a public sector context.
• Integrate Airflow workflows with external systems and services used in government operations.
• Optimize and monitor workflows using advanced debugging tools suitable for government environments.
• Leverage best practices for managing large-scale Airflow deployments within the public sector.

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

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

• Optimize Apache Airflow workflows for enhanced performance and reliability in public sector operations.
• Manage and troubleshoot complex workflow dependencies for more efficient government processes.
• Leverage advanced Airflow features, including custom operators and sensors, to support government-specific tasks.
• Scale Apache Airflow for handling larger data sets and distributed systems in a government context.
• Implement best practices for monitoring, logging, and security in Airflow environments to ensure compliance with government standards.

Apache Airflow is an open-source platform designed for authoring, scheduling, and monitoring workflows. With the release of 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 to intermediate-level data and DevOps professionals who wish to use Apache Airflow to build, automate, and scale end-to-end data pipelines for government operations.

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

• Install and configure Apache Airflow in various environments for government use.
• Author, schedule, and monitor DAGs using the TaskFlow API.
• Integrate Airflow with data sources, cloud services, and machine learning workflows.
• Manage ETL pipelines to ensure data reliability in government applications.
• Secure and scale Airflow using executors, queues, and Kubernetes for enhanced government operations.
• Apply observability and best practices for production deployments within the public sector.

Format of the Course

• Interactive lecture and discussion tailored for government professionals.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment designed for government use.

Course Customization Options

• To request a customized training for this course, please contact us to arrange specific modules and scenarios relevant to your government agency's needs.