Data Virtualization with GraphDB

Learn how to combine local graph data with data residing in SQL systems into a performant data architecture. A demonstration co-relates investment events from Crunchbase, loaded in GraphDB, to changes in stock prices, loaded in Postgresql, but virtually accessed via SPARQL.

This webinar is recorded and available on YouTube.

The Enterprise Knowledge Graphs can boost the productivity of every organization providing more efficient access to its most valuable asset – the company information. Graphs put information in context in order to provide unified access to and correct interpretation of information across different data-centric systems. We will demonstrate how GraphDB 9.5 allows one to combine the two main principle approaches in integrating data: collect (ETL) vs connect (virtualization).

The main focus of the webinar is the new data virtualization features released in GraphDB 9.5, implemented by the integration of the open-source Ontop platform. We will show practical examples of how to use it. The webinar has a very practical approach and will cover the following topics:

  • What is data virtualization and its common use cases
  • The main limitations of the data virtualization approach
  • The design principles of virtualization with GraphDB
  • How to write R2RML and OBDA file descriptors to map relational schema to graphs
  • Simple and more advanced querying, indexing and reasoning with remote data
  • Combining local and virtualized data via SPARQL federation

All these features are available in the GraphDB Free Edition!

What you will learn:

  • How to implement data virtualization with all editions of GraphDB 9.5
  • How to deep dive into the Ontop project and all its core features
  • How to resolve the dilemma to connect (virtualize) or collect (ETL) data

Who is this webinar for:

  • Any novice or advanced RDF users
  • Architects who want to build a performant data architecture with NoETL
  • Data engineers who want to automate data processing and become more efficient
  • Data scientists who need to analyze information from multiple sources

Expected duration:

  • 45 minutes presentation
  • 15 minutes Q&A session

About The Speaker

Vassil Momtchev

Vassil Momtchev


Vassil has more than 15 years in software development in various domains like life sciences, pharmaceutical, health care and telecommunication. In the past 10 years he’s mostly engaged with the development of complex enterprise knowledge management solutions that features natural language processing, text analytics, reasoning, semantics, ontology design, linked data, conceptual model design, implementation of formal grammars and graph databases.