Presented By O'Reilly and Cloudera
Make Data Work
September 26–27, 2016: Training
September 27–29, 2016: Tutorials & Conference
New York, NY

Using graph databases to operationalize insights from big data

Emil Eifrem (Neo Technology), Tim Williamson (Monsanto )
11:20am–12:00pm Wednesday, 09/28/2016
Enterprise adoption
Location: 3D 12 Level: Beginner
Average rating: ***..
(3.89, 9 ratings)

Prerequisite knowledge

  • A general understanding of data processing, including architecture patterns and technologies for analyzing, transforming, and serving data
  • What you'll learn

  • Learn how organizations worldwide can use graph databases to operationalize insights from big data
  • Explore Monsanto’s big data stack and its service-oriented graph architecture that has already handled over one billion requests and is available to over 150 developers, data scientists, and applications throughout Monsanto
  • Description

    Enterprises that pursue data-driven operations and decisions are approaching the conclusion that graph analysis capabilities will yield critical competitive advantages. However, for this impact to be fully realized, the results of any graph analysis must be available, in real time, to operational applications, data scientists, and developers across the enterprise.

    Monsanto previously attempted graph analysis using both RDBMS-based and offline batch processing techniques. In the process, Monsanto found that some couldn’t drill sufficiently deeply to result in the necessary insights; others were limited in their expressibility and therefore general usefulness outside of the data science lab; and still others weren’t able to provide answers in a short enough amount of time to be useful to the business. Monsanto finally selected a graph database used alongside a broader tech stack that includes Apache Kafka, Spark, and Oracle. This stack allows Monsanto to not just derive but also operationalize insights that have allowed it to shorten R&D cycles, better understand the dynamics of its business, and carry out certain of types of science in silico.

    Tim Williamson and Emil Eifrem draw on Monsanto’s real-world experience to explain how organizations can use graph databases to operationalize insights from big data. Tim and Emil discuss Monsanto’s big data stack, using examples from Monsanto’s substantial experience with graphs, and describe the service-oriented graph architecture that has already handled over one billion requests and is available to over 150 developers, data scientists, and applications throughout Monsanto.

    Photo of Emil Eifrem

    Emil Eifrem

    Neo Technology

    Emil Eifrem is CEO of Neo Technology and cofounder of Neo4j, the world’s leading graph database. Committed to sustainable open source, he guides Neo along a balanced path between free availability and commercial reliability. Before founding Neo, Emil was the CTO of Windh AB, where he headed the development of highly complex information architectures for enterprise content management systems.

    Photo of Tim Williamson

    Tim Williamson

    Monsanto

    Tim Williamson is a data scientist at Monsanto, where he leads a full stack data engineering team focused on creating distributed analysis capabilities around complex scientific datasets in genomics, genetics, and agronomic performance.