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 Cancun or in NobleProg corporate training centers in Cancun.
NobleProg -- Your Local Training Provider
Cancun, Punta Cancun ICC
Cancun International Convention Center, Blvd. Kukulcan Km. 9, Cancun, mexico, 77500
Work in the heart of "Punta Cancún", the main business area of the city and within the International Convention Center, wher...
Work in the heart of "Punta Cancún", the main business area of the city and within the International Convention Center, where large events and business shows are held.
This modern business center is strategically located in a busy area, surrounded by banks, hotels, restaurants, shops and a wide variety of services.
Fully equipped offices, ready to start working when you enter
Business lounge to work while traveling
Easily booked meeting rooms
Regus IT High Speed Internet Access
Secure parking
Administrative support on request
In this instructor-led, live training in Cancun, 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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