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. Lausanne onsite live GraphX trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Lausanne
Rue du Port-Franc 22, Lausanne, Switzerland, 1003
Overview
Our training facilities are located at Rue du Port-Franc 22 in Lausanne. Located on th...
Overview
Our training facilities are located at Rue du Port-Franc 22 in Lausanne. Located on the fourth floor of a well-kept office building, our premises offer enough space for successful training courses in the heart of Lausanne.
Directions
The NobleProg training facilities are located in the heart of downtown Lausanne, the centre of this vibrant city.
Parking
Cars can be parked in the area along Rue du Port-Franc 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 du Port-Franc 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 Lausanne, 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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