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 Cambridge can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Cambridge
Compass House, Cambridge, united kingdom, CB24 9AD
Vision Park is within easy reach of
Cambridge city centre and has the
advantage of excellent links with the
M11 and A14.
...
Vision Park is within easy reach of
Cambridge city centre and has the
advantage of excellent links with the
M11 and A14.
Trains from London Kings Cross arrive every 30 minutes, with a 15 minute journey by taxi to the Park. Histon Railway Station is on the boundary of Vision Park, which provides high quality, frequent local transport from St Ives to Cambridge.
Guided Bus
Vision Park lies alongside the Cambridgeshire Guided Bus which offers fast and frequent buses from Histon to Huntingdon (50 minutes), St Ives (15) and Cambridge city centre (15). Onboard, there is air conditioning, free WiFi and power for your laptop and mobile devices. Please see www.thebusway.info for further information.
In this instructor-led, live training in Cambridge, 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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