Online or onsite, instructor-led live Graph Computing training courses demonstrate through hands-on practice the various technology offerings and implementations for processing graph data, with the aim to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using graph computing approaches.
Graph Computing 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 Graph Computing training can be carried out locally on customer premises in Bhutan or in NobleProg corporate training centers in Bhutan.
NobleProg -- Your Local Training Provider
Bhutan, Thimphu - Classroom
near Le Méridien , Chorten Lam, Thimphu, Bhutan, 11001
Set in Thimphu, this classroom is well located in Chorten Lam with all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
Bhutan, Paro - Classroom
near Le Méridien Riverfront, thimphu hwy, Shaba, Paro, Bhutan, 12001
Set in Paro, this classroom is well located near Paro-Thimphu Highway around 4 km from the airport, and 7 km from Rinpung Dzong, and possess all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
Apache Jena is an open source Java framework for building Semantic Web and Linked Data applications.
In this instructor-led, live training, participants will learn how to use Apache Jena to build and deploy a Semantic Web Application.
By the end of this training, participants will be able to:
Install and configure Apache Jena
Convert and store data in RDF format
Query RDF data using SPARQL
Test and deploy a semantic web application
Audience
Developers
Data Engineers
Format of the Course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training in Bhutan, 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.
The Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) that promotes common formats for data on the World Wide Web. The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.
This instructor-led, live training in Bhutan (online or onsite) is aimed at technical persons who wish to query RDF data stored in a Semantic Web database.
By the end of this training, participants will be able to:
Understand the difference between semantic web data and relational data.
Query public datasets based on Semantic Web standards.
Model data for querying with SPARQL.
Transition a website's data to semantic web linked data.
Run SPARQL queries from within an existing application.
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