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. Vancouver onsite live GraphX trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Vancouver - Pacific Centre
701 West Georgia Street, Vancouver, Canada, V7Y 1G5
The Vancouver Pacific Centre is in one of the most prominent buildings in the heart of the downtown business core. Located in...
The Vancouver Pacific Centre is in one of the most prominent buildings in the heart of the downtown business core. Located in a modern high-rise building directly above the Pacific Centre Mall.
Vancouver - Park Place
666 Burrard Street, Vancouver, Canada, V6C 3P6
The Vancouver Park Place Centre is in the heart of the downtown financial district. located directly across the street from a...
The Vancouver Park Place Centre is in the heart of the downtown financial district. located directly across the street from a Sky train Station and transit hub.
In this instructor-led, live training in Vancouver, 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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