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. Onsite live GraphX training can be carried out locally on customer premises in Utrecht or in NobleProg corporate training centers in Utrecht.
NobleProg -- Your Local Training Provider
Utrecht
The Office Operators, Stadsplateau 7, Utrecht, Netherlands, 3521 AZ
Training courses in the very heart of Utrecht city centre, with Central Station at your doorstep. NobleProg has the use of 16...
Training courses in the very heart of Utrecht city centre, with Central Station at your doorstep. NobleProg has the use of 16 professional and modern training rooms in the Utrecht WTC Meeting & Conference center.
All rooms are equipped with LCD screens and every room offers natural day light.
The WTC Utrecht is located directly adjacent to Utrecht Central Station, within walking distance from the historical centre and has ample parking opportunities for cars as well as bicycles. This makes WTC Utrecht a dream location for organisations that are looking for the ideal place to hold their meetings.
In this instructor-led, live training in Utrecht, 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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