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 trainings in Mandalay can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Mandalay Classroom
77th x 78th Street, MaHarAungMyay Township, Mandalay, mm, 11101
The Centre located within 2 km of Sri Ganesh Temple and 2.1 km of Ma Soe Yein Nu Kyaung Monastery, 2.3 km from Mahar Thatkyar...
The Centre located within 2 km of Sri Ganesh Temple and 2.1 km of Ma Soe Yein Nu Kyaung Monastery, 2.3 km from Mahar Thatkyarthiha Pagoda. Cultural Museum & Library is 2.4 km from the hotel and Mandalay University is 2.4 km away, Jade Market is 2.6 km from the accommodation. The nearest airport is Mandalay International Airport, 38 km from Gold Leaf Hotel. Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
In this instructor-led, live training in Mandalay, 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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