Available online or onsite, these instructor-led live Apache Airflow training courses provide interactive, hands-on practice in building and managing end-to-end data pipelines using Apache Airflow.
Apache Airflow training is offered as either "online live training" or "onsite live training". Online live training (also known as "remote live training") is delivered through an interactive remote desktop. Onsite live training can take place at customer premises in Gaia or within NobleProg corporate training centers in Gaia.
NobleProg -- Your Local Training Provider
Holiday Inn Porto - Gaia
220, Rua Diogo Macedo, 220, Vila Nova de Gaia 4400-107 , Gaia, Portugal
From Porto by Metro
If you're coming from Porto city centre:
Take the Metro Line D (yellow line) toward Vila d’Este.
Get off at João de Deus.
From João de Deus, walk about 13 minutes to: Rua Diogo Macedo 220, 4400-107 Vila Nova de Gaia.
From Vila Nova de Gaia train station
If by “local train station” you mean Vila Nova de Gaia-Devesas:
The address is approximately 12 minutes walking from Vila Nova de Gaia station according to the accommodation's location information.
You can therefore walk directly rather than taking the metro.
Conducted by an expert instructor, this live training in Gaia (online or on-site) is designed for advanced participants aiming to deploy Apache Airflow in cloud settings, configure CI/CD pipelines, and implement comprehensive monitoring and logging strategies.
By the conclusion 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 Gaia (online or onsite) is designed for intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.
Upon completion of this training, participants will be capable of:
Configuring Apache Airflow to orchestrate machine learning workflows.
Automating tasks related to data preprocessing, model training, and validation.
Integrating Airflow with various machine learning frameworks and tools.
Deploying machine learning models through automated pipelines.
Monitoring and optimising machine learning workflows in production environments.
This instructor-led, live training in Gaia (online or onsite) targets advanced-level participants seeking to develop custom operators, sensors, and plugins within Apache Airflow, and to integrate these components with existing data systems for sophisticated automation and monitoring.
Upon completion of this training, participants will be capable of:
Developing custom operators, hooks, and sensors tailored to specific workflows.
Designing and implementing Airflow plugins to extend platform functionality.
Integrating Airflow workflows with external systems and services.
Optimizing and monitoring workflows using advanced debugging tools.
Applying best practices for managing large-scale Airflow deployments.
This instructor-led, live training in Gaia (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.
This instructor-led, live training in Gaia focuses on Apache Airflow 2.x for the construction, automation, and scaling of data pipelines. Participants will acquire skills in authoring DAGs, integrating cloud services, and implementing observability, security, and Kubernetes-based scaling to ensure 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
Provisional Upcoming Courses (Contact Us For More Information)
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