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. Toulouse onsite live GraphX trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Toulouse, City Center
Mutliburo Toulouse, 9 Rue de Sébastopol, Toulouse, france, 31000
The business center is located in the Compans business district on the edge of Compans Caffarelli Park, which extends over...
The business center is located in the Compans business district on the edge of Compans Caffarelli Park, which extends over 10 hectares.
Many businesses have settled in this area, attracted both by the situation between the historic heart and the ring road that leads to the airport and the modernity of the site. Note the proximity of the conference center of Toulouse.
The business center offers its fully equipped offices and 2 meeting rooms to organize trainings and business meetings. All rooms are air-conditioned. Your visitors will be welcomed by our teams who will organize your day (preparation of rooms, internet connections, coffee break, catering, secretarial, hotel booking
In this instructor-led, live training in Toulouse, 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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