Online or onsite, instructor-led live GraphX training courses demonstrate through hands-on practice how to implement GraphX to carry out graph computing across many machines in parallel.
GraphX training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Geneva onsite live GraphX trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Geneva
Rue de Malatrex 14, Geneva, Siwtzerland, 1201
Overview
Our training facilities are located at Rue de Malatrex 14 in Geneva. Located on the fo...
Overview
Our training facilities are located at Rue de Malatrex 14 in Geneva. Located on the fourth floor of a well-kept office building, our premises offer enough space for successful training courses in the heart of Geneva, within walking distance of the Rhone.
Directions
The NobleProg training facilities are located in the heart of downtown Geneva, the centre of this vibrant city.
Parking
Cars can be parked in the area along Rue de Malatrex and the nearby side streets, even if you may have to search for a moment. There is no charge for parking.
Local Amenities
Around the Rue de Malatrex there are numerous small restaurants and shops where you can eat well and cheaply. There are also some hotels close by if you need accommodation for the training.
In this instructor-led, live training in Geneva, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.
By the end of this training, participants will be able to:
Understand how graph data is persisted and traversed.
Select the best framework for a given task (from graph databases to batch processing frameworks.)
Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.
View real-world big data problems in terms of graphs, processes and traversals.
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