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Graph Database Engine Shoot-out
For all of those who weren't able to catch it at the inaugural Graph Day (http://graphday.com/), we're offering an encore presentation of Josh Perryman's Graph Database Engine shoot out. This is a great talk with a surprising conclusion. Abstract Our client’s legacy system held graph-like data in a relational database, but new customers’ data sizes were crippling performance and scale. As part of an overall architectural rejuvenation, we evaluated migrating their data to graph and relational schemas to determine if query performance and scalability could be improved. With representative data in hand, we designed alternate relational schemas, graph database designs, and triple store designs, benchmarking performance and noting subjective measures such as ease-of-use and fluency of the query language. Vendors included PostgreSQL, Neo4J, Titan, and AllegroGraph. Follow-up studies included other vendors. The results surprised us, leading to a hybrid relational and graph recommendation. We have implemented the first milestone over the last year. Follow-up work shows that graph DB vendors have come a long way even in that time. This methodology and information in this case study should be useful to teams choosing a database engine, whether graph or relational, for their next project. Speaker Bio Josh Perryman (https://www.linkedin.com/pub/josh-perryman/8/20/565) (@joshperryman (https://twitter.com/joshperryman)) likes to play with data. Oftentimes this is implementing proprietary algorithms closer to the data for performance or scale. Sometimes it is ad-hoc investigation and analysis, a sort of exploratory querying. A few times he’s been able to leverage his experience with data engines for dramatic performance improvements. But the real joy is designing a schema for both functionality and performance, one which increases the productivity of other developers and enables a technology to solve new problems or deliver new value to the business. But technology isn't just data, and he does more than just play with data. He’s worked with high performance computing (HPC) environments, taking computations from hours to minutes or seconds. He has built visualizations which deliver new insights into complex data domains. He’s managed technology personnel, both directly and indirectly, to deliver technology solutions. He’s have put together more types of technology components, software and hardware, than can be counted, because one of his fortes is solving problems by building sustainable systems. Agenda 6:30 Networking 7:00 Featured talk 8:45 Adjourn to pub

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Midtown · New York, NY

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    What we're about

    Public Group

    NY Graph is a place for people to learn about and discuss graph structures, graph theory, graph databases and related topics. We host technical presentations and lectures, product demos, lightning talks, seminars, town hall-style open forums and hands-on tutorials, in addition to less-structured monthly gatherings.

    Why graphs? Why now?
    Graph structures and methods are being used everywhere. As the size of datasets being generated, processed, and presented continues to grow, the relative strengths and weaknesses of different data structures, including graphs, have become more and more significant in everyday applications. Meanwhile, wider awareness of graph concepts (thanks in large part to the popularity of online social networks and widespread adoption of machine learning techniques) has increased the use of graph methods in a variety of fields. As a result, the last few years have seen renewed interest in academic circles, and a large number of companies and independent efforts putting a great deal of effort into creating faster, more scalable, and more reliable graph databases and ever more powerful interfaces.

    Am I missing something? What are graphs?
    The following Wikipedia entries provide a decent primer:
    http://en.wikipedia.org/wiki/Graph_theory
    http://en.wikipedia.org/wiki/Graph_(data_structure)
    http://en.wikipedia.org/wiki/Graph_database

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