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. Mexico City onsite live GraphX trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Mexico City - Colonia Del Valle
Av. Insurgentes Sur N° 800 , Mexico City, mexico, 3100
The Colonia Del Valle (HQ) Center is on the eighth floor of an attractive, 20-story steel-gray building on the Avenida de los...
The Colonia Del Valle (HQ) Center is on the eighth floor of an attractive, 20-story steel-gray building on the Avenida de los Insurgentes. The landmark Mexicana de Aviatión Tower is nearby, and the World Trade Center (WTC) is only two blocks away.
Mexico City - Torre Angel
Paseo de la Reforma 350, Mexico City, mexico, 6600
The Mexico City Torre Angel Center is on the 10th and 11th floors of a prestigious curved, reflective high-rise building with...
The Mexico City Torre Angel Center is on the 10th and 11th floors of a prestigious curved, reflective high-rise building with magnificent views to The Angel, the iconic monument to independence on the Paseo de la Reforma.
In this instructor-led, live training in Mexico City, 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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