Ravel

Ravel, an Austin, Texas-based company, wants to provide a supported, open-source version of Google's Pregel software called GoldenOrb to handle large-scale graph analytics. Ravel COO Zach Richardson told me at our Structure: Big Data event last week that the company would release the GoldenOrb code on March 31 and explained how such databases can help run analytics on massive amounts of data across huge numbers of nodes without taking as much time as Hadoop or MapReduce.

While Pregel remains an in-house technology for Google, the data startup Ravel is releasing its Pregel-like, large-scale graph processing technology today. GoldenOrb, which Ravel is also open sourcing (GitHub link), will solve some of the same types of problems as Pregel, but can be applied to many other areas beyond network analysis and social graph analysis, such as epidemiology and mathematics.

Responding to the need for layering in more relational analysis amongst data points, systems like Pregel emerged from Page Rank’s two-dimensional MapReduce. And just as Hadoop was spawned from a similar pet project at Yahoo, GoldenOrb hopes to extend the Pregel model for massive, scalable graph processing in the cloud.

Analytics company Ravel has announced it is releasing GoldenOrb, its massive-scale graph analysis software, as open source. GoldenOrb is based on the ideas behind Google's Pregel architecture which is in turn inspired by the Bulk Synchronous Parallel Model developed in the 1980s.

This week, a Texas startup known as Ravel unveiled an open source project based on Google's 2010 paper describing Pregel. Open sourced under an Apache license at GitHub, the project is dubbed GoldenOrb.

A few years back, while working on a PhD in computational mathematics, Ravel president and GoldenOrb lead architect Zach Richardson helped found a small company that basically helped other businesses processes large amounts of data, including "semantic web" data, which seeks to give machines a better "understanding" of text on the internet. They soon realized that to solve such problems required better tools.

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