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 Dehiwala- Mount Lavinia or in NobleProg corporate training centers in Dehiwala- Mount Lavinia.
NobleProg -- Your Local Training Provider
Dehiwala
No 154, Hotel Road, Dehiwala- Mount Lavinia, sri lanka, 10370
The Centre located in the coastal town of Mount Lavinia, which is just 12 kilometres south of central Colombo and 54 kilometr...
The Centre located in the coastal town of Mount Lavinia, which is just 12 kilometres south of central Colombo and 54 kilometres from the Bandaranaike International Airport and 4-minute walk from the Mount Lavinia railway station, and 4 km from the National Zoological Gardens of Sri Lanka. 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 Dehiwala- Mount Lavinia, 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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