Josh Perryman to present at GraphDay Austin!

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Josh Perryman has been accepted to present at the inaugural GraphDay in Austin, TX on January 17, 2016. Here’s his 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.

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Sebastian Good

September 25, 2015

Josh Perryman to present at GraphDay Austin!

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Josh Perryman has been accepted to present at the inaugural GraphDay in Austin, TX on January 17, 2016. Here’s his 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.

User Audience

Services

Project Details

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