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 Coventry can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Coventry
Warwick Road, Coventry, united kingdom, CV1 2DY
The business centre is located within the sought after professional district of the city centre of Coventry. The workspace is...
The business centre is located within the sought after professional district of the city centre of Coventry. The workspace is within a five minute walk of Coventry train station, public transport routes, car parking facilities (we also have onsite car parking), banks, estate agents and city council offices.
Based in the midlands the centre is has convenient access to both main road, motorway and rail networks with only a short commute to Birmingham International and Coventry airports.
In this instructor-led, live training in Coventry, 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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