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 trainings in Yokohama can be carried out locally on customer premises or in NobleProg corporate training centers.
The centre is located within a 5-minute walk from JR Kannai station and a 3-minute walk from Yokohama Blue Line Subway’s Ka...
The centre is located within a 5-minute walk from JR Kannai station and a 3-minute walk from Yokohama Blue Line Subway’s Kannai station. On the north side of the centre is the traditional business district lined with governmental offices, including the prefectural office, Yokohama’s city hall, customs , and port authorities. The area is home to a large number of import / export companies. The city is known for its international air which attracts expats as well as the good old cultural heritage loved by tourists. Tourism thrives here, with many attractions, hotels near the waterfront and the retro downtown area of Motomachi. Yokohama station, one of the biggest hubs in the Tokyo metropolitan area is less than a 10 minute train-ride from this centre. Yokohama station connects to many railway companies and you will reach Tokyo or Shibuya station in 30 minutes.
Apache Jena is an open source Java framework for building Semantic Web and Linked Data applications
In this instructorled, 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 handson practice
Note
To request a customized training for this course, please contact us to arrange
.
Blazegraph is an open source, Javabased RDF graph database for storing and representing data with complex relationships It supports Blueprints and RDF/SPARQL 11
In this instructorled, live training, participants will learn how to use Blazegraph capture complex data in graph format for retrieval from a number of sample applications All exercises will be carried out handson in a livelab environment
By the end of this training, participants will be able to:
Install and configure Blazegraph in standalone mode, clustered mode (optional) or embedded mode (optional)
Create, test and deploy a sample application to query complex data in a Blazegraph data store
Understand how to leverage GPU (graphics processing unit) to accelerate computations
Audience
Developers
Format of the Course
Part lecture, part discussion, exercises and heavy handson practice
Note
To request a customized training for this course, please contact us to arrange
.
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 Yokohama (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.
In this instructor-led, live training in Yokohama, 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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