New Relic Aims to Revolutionize App Analytics

New Relic’s Rubicon will cross the river (in beta form) in 2014.

Developing software is a data-intensive process, with developers expected to wrestle with everything from transactions and end-user experiences to mobile activity. Better insights into all that data can help improve the development process, but actually analyzing all those disparate datasets is a real challenge. How can you best analyze an app for customer churn, for example, or adoption in the wake of a new feature addition?

New Relic has an idea for solving those issues: a platform it’s codenamed Rubicon, which it claims can provide real-time insight into the cloud of disparate data surrounding application development. The company suggests that, in doing so, it’ll achieve what a hodgepodge of NoSQL Hadoop distributions, Big Data tools, and specialized dashboards have failed to accomplish over the years. That’s a bold and ambitious claim, and at this point it’s too early to tell how the effort will pan out—but the idea that a single platform can provide comprehensive insight into the data-centric aspects of app development is a powerful one.

In an interview with VentureBeat, New Relic CEO Lew Cirne said that the idea of building a new database came to him during a family vacation in 2012, and that he isolated himself for weeks afterwards to work on it. He placed usability high on the priority list, focusing on the dashboard and visualization tools, but powerful speed is Rubicon’s true selling point.

“If we measure 10,000 pageviews in one second, we can distill it down to 100 pieces of data and therefore do it at high scale,” he told the Website. “But you lose the ability to do these histograms and percentiles. To do this, we created an entirely new analytics product for New Relic.” The platform, delivered as SaaS (Software-as-a-Service), will arrive in Beta sometime in 2014.

If New Relic succeeds in its effort, it could drive more companies to develop platforms that unite disparate forms of data onto a single platform. Already several firms are experimenting with making data analytics faster; SAP, for example, has spent quite some time pushing its HANA in-memory technology, which speeds up the company’s business-intelligence practices. But the popularity of the so-called Big Data movement means there are a lot of datasets floating out there, in a variety of formats, along with a growing number of tools that perform radically different analytics functions—in that context, any new software that contributes some level of cohesion is probably a good thing.

 

Image: New Relic

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