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 Vienna or in NobleProg corporate training centers in Vienna.
NobleProg -- Your Local Training Provider
NobleProg Wien
Modecenterstraße 22, Wien, Austria, 1030
Overview
Our training facilities are located at Modecenterstrasse 22 in Vienna. Our spaci...
Overview
Our training facilities are located at Modecenterstrasse 22 in Vienna. Our spacious training rooms offer optimal training conditions for your needs.
For questions and training registrations, please contact our company headquarters:
NobleProg Europe GmbH,
Kolonnenstr. 8
10827 Berlin
Deutschland
Directions
The NobleProg training facilities are conveniently located near the A23 motorway and the nearest subway station, within walking distance, is the Zippererstrasse station.
Parking spaces
There are parking spaces in the surrounding streets around our training rooms.
Local infrastructure
There are numerous restaurants within walking distance.
In this instructor-led, live training in Vienna, 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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