Local, instructor-led live Graph Database training courses demonstrate through hands-on practice how Graph Database uses graph theory to store, map and query relationships.
Graph Database training is available as "onsite live training" or "remote live training". Onsite live Graph Database training can be carried out locally on customer premises in San Diego or in NobleProg corporate training centers in San Diego. Remote live training is carried out by way of an interactive, remote desktop.
Learn Graph Database in our training center in San Diego. Read more
Testimonials
★★★★★
★★★★★
Flexibility to blend in with Automata related details to get more of a real world scenario as we went on.
Autodata Ltd
Course: Beyond the Relational Database: Neo4j
The amount of knowledge we got.
Course: Beyond the Relational Database: Neo4j
The trainer did bring some good insight and ways to approach developing a graph database. He used examples from the slides presented but also drew on his own experience which was good.
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.
FlockDB is an open source distributed, fault-tolerant graph database for managing wide but shallow network graphs. It was initially used by Twitter to store relationships among users.
In this instructor-led, live training, participants will learn how to set up and use a FlockDB database to help answer social media questions such as who follows whom, who blocks whom, etc.
By the end of this training, participants will be able to:
- Install and configure FlockDB - Understand the unique features of FlockDB, relative to other graph databases such Neo4j - Use FlockDB to maintain a large graph dataset - Use FlockDB together with MySQL to provide provide distributed storage capabilities - Query, create and update extremely fast graph edges - Scale FlockDB horizontally for use in on-line, low-latency, high throughput web environments
Audience
- Developers - Database engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training in San Diego, 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.
In this instructor-led, live hands-on training in San Diego, we will set up a live project and put into practice the skills to model, manage and access your data using neo4j. We contrast and compare graph databases with SQL-based databases as well as other NoSQL databases and clarify when and where it makes sense to implement each within your infrastructure.
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