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. Porto Alegre onsite live GraphX trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Porto Alegre - Platinum Building
Carlos Gomes Avenue, 700, Porto Alegre, Brazil, 90480-000
Platinum Building has many amenities close by. The Boa Vista district is a major corporate center in this sophisticated and progressive port city. Also the business center is six kilometers from the international airport and well served by public transport.
Porto Alegre - Guaiba Business Center
Av. Dolorez Alcaraz Caldas, 90 Porto Alegre RS, Praia de Belas, Brasil, 90110-180
Be at the centre of a lively business district in Praia De Belas, surrounded by courthouses and government offices. Centro Empresarial Guaiba is a distinctive Class A building in the thriving state capital of Porto Alegre.
In this instructor-led, live training in Porto Alegre, 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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