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 Regina can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Regina - Royal Bank Building
2002 11th Ave, Regina, Canada, S4P 0J3
Grow your business in one of Regina’s most prominent finance hubs. Build your base in the prestigious Royal Bank Building, ...
Grow your business in one of Regina’s most prominent finance hubs. Build your base in the prestigious Royal Bank Building, home to several leading financial and professional services companies.
Impress your clients and colleagues with the building’s commanding city views, before getting to work in stylish surroundings with plenty of natural light. And travel easily for work or pleasure, with the nearby Regina International Airport offering regular flights across Canada and beyond.
In this instructor-led, live training in Regina, 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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