Tim Gasper, Product Manager at Infochimps, covers five new open source technologies that are ‘shaking up’ big data including graph database technologies.

Gremlin and Giraph help empower graph analysis, and are often used coupled with graph databases like Neo4j or InfiniteGraph, or in the case of Giraph, working with Hadoop. Golden Orbis another high-profile example of a graph-based project picking up steam. Graph databases are pretty cutting edge. They have interesting differences with relational databases, which mean that sometimes you might want to take a graph approach rather than a relational approach from the very beginning. The common analogue for graph-based approaches is Google’s Pregel, of which Gremlin and Giraph are open source alternatives. In fact, here’s a great read on how mimicry of Google technologies is a cottage industry unto itself. Why should you care? Graphs do a great job of modeling computer networks, and social networks, too — anything that links data together. Another common use is mapping, and geographic pathways — calculating shortest routes for example, from place A to place B (or to return to the social case, tracing the proximity of stated relationships from person A to person B). Graphs are also popular for bioscience and physics use cases for this reason — they can chart molecular structures unusually well, for example. Big picture, graph databases and analysis languages and frameworks are a great illustration of how the world is starting to realize that Big Data is not about having one database or one programming framework that accomplishes everything. Graph-based approaches are a killer app, so to speak, for anything that involves large networks with many nodes, and many linked pathways between those nodes. The most innovative scientists and engineers know to apply the right tool for each job, making sure everything plays nice and can talk to each other (the glue in this sense becomes the core competence).
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