About the Reviewers

Taffy Brecknock has worked in the IT industry for more than 20 years. During his career, he has worked as a software developer, managed development teams, and has been responsible for application design and more recently systems architecture.

He has held roles with both public and private sector organizations. While working with the Australian Government, Taffy got first-hand exposure to the use of connected data in law enforcement. After using relational database systems as the data repository, he is experienced in the short comings of using this paradigm to model such systems.

After learning about graph databases, specifically Neo4j, he has become extremely interested in the many different applications of this technology. He feels that there are few problems in today's business world that cannot benefit from being modeled in a graph.

Jose Ernesto Echeverria started working with relational databases in the 90s, and has been working with Neo4j since 2014. He prefers graph databases over others, given their capabilities for real-world modeling and their adaptability to change. As a polyglot programmer, he has used languages such as Java, Ruby, and R with Neo4j in order to solve data management problems of multinational corporations. He is a regular attendee of GraphConnect, OSCON, and RailsConf. When not working, he enjoys spending time with family, road trips, Minecraft projects with his children, as well as reading and drinking craft beers.

Adriano Longo is a freelance data analyst based in the Netherlands with a passion for Neo4j's relationship-oriented data model.

He is specialized in querying, processing, and modeling data with Cypher, R, Python, and SQL and has worked on climate prediction models at UEA's Climatic Research Unit before focusing on analytical solutions for the private sector.

Today, Adriano uses Neo4j and Linkurious.js to explore the complex web of relationships that nefarious actors use to obfuscate their abuse of environmental and financial regulations--making dirty secrets less transparent, one graph at a time.