Whether conducted online or on-site, these instructor-led live Apache Airflow workshops focus on practical, hands-on exercises that show how to leverage Apache Airflow for constructing and overseeing complete data pipelines.
Apache Airflow instruction is offered through "online live training" or "onsite live training". Online live training (also referred to as "remote live training") is facilitated via an interactive remote desktop. Onsite live training can take place directly at the client’s location in Prague or within NobleProg’s corporate training facilities in Prague.
From Prague Main Train Station (Praha hlavní nádraží)
Take tram 9 from Hlavní nádraží toward Sídliště Řepy.
Get off at Újezd.
Walk about 10 minutes toward Malostranské náměstí / Prokopská.
Continue along Prokopská to 296/8.
Alternative: Take the metro C from Hlavní nádraží → Muzeum, change to metro A → Malostranská, then walk across Malá Strana. This involves more walking.
From Prague Bus Station — Florenc
Take metro B from Florenc toward Zličín.
Get off at Můstek.
Change to metro A toward Nemocnice Motol.
Get off at Malostranská.
Walk approximately 10–15 minutes to Prokopská 296/8.
This instructor-led, live training in Prague (online or onsite) is designed for advanced participants aiming to deploy Apache Airflow in cloud settings, configure CI/CD pipelines, and implement strong 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 Prague (online or onsite) is designed for intermediate-level participants seeking 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 tasks related to 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 a production environment.
This instructor-led, live training in Prague (available online or on-site) targets advanced-level participants who aim to create custom operators, sensors, and plugins in Apache Airflow and integrate them 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 Prague (available online or on-site) is designed for intermediate-level participants who aim to optimize 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 improved performance and reliability.
Manage and troubleshoot complex workflow dependencies.
Leverage 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.
This live, instructor-led session in Prague explores Apache Airflow 2.x for the construction, automation, and scaling of data pipelines. Learners will master the creation of DAGs, integration of cloud services, and the implementation of observability, security, and Kubernetes-based scaling to maintain robust production environments.
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Testimonials (1)
The instructor adapted the training to the participants’ level and responded to all questions. He was very communicative, and it was easy to interact with him. I really appreciated the format of the training, which included many practical exercises. Overall, it was a very engaging and well-organized session.
Jacek Chlopik - ZAKLAD UBEZPIECZEN SPOLECZNYCH
Course - Apache Airflow: Building and Managing Data Pipelines
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