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 Burnaby or in NobleProg corporate training centers in Burnaby.
NobleProg -- Your Local Training Provider
Burnaby - Metrotown
Suite 2600, 4720 Kingsway, Burnaby, Canada, V5H 4N2
The Burnaby Metrotown Centre is a 30-storey office building which is centrally located by Metrotown Sky train station and con...
The Burnaby Metrotown Centre is a 30-storey office building which is centrally located by Metrotown Sky train station and connected to the Metropolis shopping mall in Burnaby.
Burnaby Centre
Suite 200, 4170 Still Creek Drive , Burnaby , Canada, V5C 6C6
The Burnaby Centre is in a premier business park in North Burnaby. It is located close to Highway 1 and within walking distan...
The Burnaby Centre is in a premier business park in North Burnaby. It is located close to Highway 1 and within walking distance of Brentwood Town Centre and Gilmore SkyTrain Station.
In this instructor-led, live training in Burnaby, 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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