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. Hanoi onsite live GraphX trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Ly Thai To
Level 5, 41A Ly Thai To Street, Hanoi, Vietnam
The business centre is conveniently located in Sentinel Place on the corner of Ly Thai To and Tran Nguyen Han Streets. It is ...
The business centre is conveniently located in Sentinel Place on the corner of Ly Thai To and Tran Nguyen Han Streets. It is located within five minutes walk to the Opera House, the Metropole and the Hilton Hanoi Opera and in close proximity to The Ministry of Finance, The Hanoi People's Committee, The State Bank of Vietnam and numerous other commercial properties and foreign banks. Ly Thai To Street is a prominent street which is well known in Hanoi and provides easy of access for any visitors. Key office developments in the vicinity include BIDV Tower, 63 Ly Thai To and the Opera Business Centre and there is high-end shopping malls in the immediate locality.
In this instructor-led, live training in Hanoi, 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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