Graph Database Training Courses in Luxembourg

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 Luxembourg, 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 Luxembourg, 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.