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 Salzburg can be carried out locally on customer premises or in NobleProg corporate training centers.
Our training facilities are located atAdolf-Kolping-Str. 10 in Salzburg. Our spacious ...
Overview
Our training facilities are located atAdolf-Kolping-Str. 10 in Salzburg. Our spacious training rooms are located near the Salzach river, within walking distance of the old town, and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the A1 motorway and the train station and Europark Salzburg Taxham train station are easily accessible.
Parking
Parking is available in the surrounding streets around our training rooms.
Local Services
In the area of the city centre there are numerous restaurants and also hotels are within walking distance.
In this instructor-led, live training in Salzburg, 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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