Blazegraph: Creating a Graph Database Application Training Course

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.

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 the UAE (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.

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 the UAE, 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.

This instructor-led, live training in the UAE (online or onsite) is aimed at engineers who wish to use JanusGraph to process very large graphs that require abnormal storage and computational capacity.

By the end of this training, participants will be able to:

• Install and configure JanusGraph.
• Integrate JanusGraph with multiple backend storage systems (Cassandra, HBase, etc.) and multiple indexing software (Elasticsearch, Solr, etc.). 
• Configure multiples machines into a cluster for use by JanusGraph.
• Query the database using the Gremlin query language.
• Process graph data at scale, beyond what a single machine can provide.
• Support thousands of concurrent users traversing graph data in real time.
• Query graph data for analysis.

In this instructor-led, live hands-on training in the UAE, 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.

This instructor-led, live training in the UAE (online or onsite) is aimed at developers who wish to use Neo4j AuraDB graph database to build cloud applications with high availability and zero administration.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start developing graph database applications with Neo4j AuraDB.
• Understand the features, core concepts, and architecture of Neo4j AuraDB.
• Learn how to build and scale graph database applications in the cloud.
• Enhance cloud security with AuraDB's pre-configured authentication and encryption features.
• Migrate existing Neo4j databases to AuraDB.