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 Liege or in NobleProg corporate training centers in Liege.
NobleProg -- Your Local Training Provider
Liège, Place des Guillemins
NobleProg Liège, 2 Place des Guillemins, Liège, Belgium, 4000
Luik is de hoofdstad van de provincie Luik en de economische hoofdstad van Wallonië. Door het aantal inwoners is het de eers...
Luik is de hoofdstad van de provincie Luik en de economische hoofdstad van Wallonië. Door het aantal inwoners is het de eerste Waalse agglomeratie, de derde agglomeratie van België na Brussel en Antwerpen en de vierde gemeente na Antwerpen, Gent en Charleroi. Luik is lange tijd een grote industriestad geweest, maar sinds de jaren zestig heeft het een lange neergang ondergaan, waarbij de fabrieken in verval raken. Luik is ook sterk afhankelijk van transport en multimodaliteit. Luik is een belangrijk economisch centrum met zijn autonome haven, de tweede binnenhaven in West-Europa en zijn luchthaven gespecialiseerd in vracht. In feite blijft de stad de economische hoofdstad van Wallonië.
In this instructor-led, live training in Liege, 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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