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 Paris can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Paris, Opera Bourse
Nobleprog Paris, 15 Rue Taitbout, Paris, france, 75009
At the corner of Boulevard Haussmann and Rue Taitbout, the Multiburo Opera-Bourse business center is located in the heart ...
At the corner of Boulevard Haussmann and Rue Taitbout, the Multiburo Opera-Bourse business center is located in the heart of the largest financial center in Paris, but also within walking distance of the department stores. This area between the Paris Opera and the Bourse is always very attractive due to its cultural aspects and its economic dynamism. The establishment of seats of international groups (in the financial and insurance sector) shows the renown of the places
Access:
Train station
10 min by foot far from Saint-Lazare
In this instructor-led, live training in Paris, 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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