"An Introduction to InfiniteGraph, Connecting the Dots to Find Meaning in Big Data" Webcast - Replay Now Available

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DBTA recently hosted a webcast  to examine the technology behind InfiniteGraph, the distributed and scalable graph database. 

Host Tom Wilson, president of DBTA and Unisphere Research, set the stage for the webcast by noting that recent Unisphere Research surveys have found that more than 10% of respondents' enterprises now report having more than a petabyte of live data and that data is growing at more than 50% a year at 16% of Oracle sites, as of late 2010. Wilson also said that a recent study among DBTA subscribers revealed that 43% of respondents are evaluating new data management strategies and nearly 20% are currently evaluating NoSQL databases.

Big data problems are presenting themselves in almost every area of computing from social network analysis to file processing. Many technologies, such as those in the NoSQL space, were developed in response to the limitations of current storage systems as an effective mechanism to deal with these mountains of data. And much of that data is interconnected in ways that, when organized properly, gives interesting and often valuable information.

A key technical attribute of graph databases is that everything about the database is optimized around data relationships, Darren Wood, InfiniteGraph architect and lead developer, explained as part of his overview of graph databases. While that may sound like a relational database, it is actually quite different, he emphasized. The second thing that sets graph databases apart is the way that the API is focused directly around graph problems.

According to Wood, distributed graph must-haves include high performance distributed persistence; the ability to deal with remote data reads (fast); the intelligent local cache of subgraphs; distributed navigation processing; distributed, multi-source concurrent ingest; and write modes supporting  both strict and eventual consistency.

Mark Maagdenberg, InfiniteGraph SE, highlighted use cases and markets that are highly interested in graph databases, emphasizing that graph databases are used everywhere. Graph databases are used for social network analysis; targeted advertising; recommendation engines; transportation; and network analysis. In addition, it is used for fraud detection and prevention and also crime detection/prevention, which are very hot topics especially in the government agency arena with regard to preventing terrorism, Maagdenberg noted.

InfiniteGraph was designed specifically to traverse connections and provide the framework for a new set of products built to provide real-time business decision support and relationship analytics. Objectivity, Inc. has just released a commercial version of InfiniteGraph and a downloadable version is available now at

A replay of "An Introduction to InfiniteGraph, Connecting the Dots to Find Meaning in Big Data,"  and follow-up Q&A session with Wood and Maagdenberg, and webcast slidedeck, is accessible on the DBTA website.