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. Marseille onsite live GraphX trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Marseille, Prado Quarter
Mutliburo Marseille, 565 Avenue du Prado, Marseille, france, 13008
At the heart of the prestigious business quarter of Avenue Prado, surrounded by nature and offering a beautiful view on the s...
At the heart of the prestigious business quarter of Avenue Prado, surrounded by nature and offering a beautiful view on the see.
The business center offers companies a wide range of services and technical means with advanced technology, thus encouraging the development of the activity of its customers.
Plane: 40 minutes away fromInternational Airport Marseille-Provence.
Bus: 19 et 83 lines. "La Plage" Station.
Tram: Line 2. Stop " Rond-Point du Prado"
In this instructor-led, live training in Marseille, 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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