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. Brisbane onsite live GraphX trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
200 Mary Street, Brisbane
Level 16, 200 Mary Street, Brisbane, Australia, 4000
The 200 Mary Street business centre is located only 125 metres from the Brisbane GPO, strategically located within Brisbaneâ€...
The 200 Mary Street business centre is located only 125 metres from the Brisbane GPO, strategically located within Brisbane’s premier financial district known as ‘the Golden Triangle’. Situated on Level 16 and boasting views over the Brisbane River, hinterland and the CBD, the centre provides the ideal setting to showcase your business in a contemporary environment with fully-equipped work space.
200 Mary Street is immersed in Brisbane’s city life with everything at your doorstep, from key transport links to restaurants and bar as well as hotels. The centre is serviced by the City Cat service and Central Train Station, which is less than five minutes walk.
In this instructor-led, live training in Brisbane, 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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