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 Graz can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
NobleProg Graz
Waagner-Biro-Strasse 47, Graz, Austria, 8020
Overview
Our training facilities are located at Waagner-Biro-Strasse 47 in Graz. Our spac...
Overview
Our training facilities are located at Waagner-Biro-Strasse 47 in Graz. Our spacious training rooms are located directly in the old town and offer optimal training conditions for your needs.
Directions
The NobleProg training facilities are best reached via the A9 motorway and the federal highway 67.
Parking spaces
There are parking spaces in the streets around our training rooms as well as the ContiPark multi-storey car park.
Local infrastructure
There are numerous restaurants in the downtown area and hotels are also within walking distance.
In this instructor-led, live training in Graz, 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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