Graph Computing Training in San Francisco Bay Area
Local, 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 "onsite live training" or "remote live training". San Francisco Bay Area onsite live Graph Computing trainings can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.
Learn Graph Computing in our training center in San Francisco Bay Area. The Sunnyvale center is located one block from Caltrain rail station and provides easy access to Central Expressway and Highway 101.
It is in walking distance to the city's downtown district, featuring numerous restaurants, shops, and businesses, as well as a short drive from San Jose International Airport. Read more
Learn Graph Computing in our training center in San Francisco Bay Area.
With its fabulous arched windows, the 75 Broadway office, located in the heart of Jackson Square, is designed to impress. Situated at Golden Gateway Commons on the second floor of this unique campus-style building, it overlooks the green terrains of Sydney Walton Park with the famous waterfront piers behind. This enviable location is a short walk from public transport and just blocks away from the Embarcadero and the ferry building, as well as a host of convenient amenities....
Blazegraph is an open source, Java-based RDF graph database for storing and representing data with complex relationships. It supports Blueprints and RDF/SPARQL 1.1.
In this instructor-led, live training, participants will learn how to use Blazegraph to capture complex data in graph format for retrieval from a number of sample applications. All exercises will be carried out hands-on in a live-lab 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 hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training in San Francisco Bay Area, 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 San Francisco Bay Area (onsite or remote) 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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