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 Antwerp can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Antwerp
Hotel NH Collection Antwerp Centre, Pelikaanstraat 84, 2018 Antwerpen, Belgium, Antwerp, Belgium, 2018
The hotel has a conference capacity for up to 110 attendees, with 6 dedicated, flexible spaces available. A range of smaller...
The hotel has a conference capacity for up to 110 attendees, with 6 dedicated, flexible spaces available. A range of smaller boardrooms and a larger conference room is available for large and small meetings. The facilities include up-to-date audio-visual equipment, with refreshments available to order and an events planner on site for assistance. Unusually for a city center hotel, the hotel offers parking in its own garage.
In this instructor-led, live training in Antwerp, 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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