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Understanding The Next Generation Of Analytics: Cazena’s Big Data Infrastructure Compiler

22 February, 2017

Understanding The Next Generation Of Analytics: Cazena’s Big Data Infrastructure Compiler

Technological innovation compounds over time, with new advances building on the foundations laid by the inventions of the past. At the Spark East summit this past week, it was fascinating to see this dynamic play out in practice. Many of the higher level services in the spotlight at the conference are evolutionary next steps from lower level services created in the past. This is key to a well-known theory from Ray Kurzweill about how the exponential growth of technology occurs: Combining lower level services into higher order solutions creates true innovative acceleration.

I found this theory highly relevant to one product example from the summit. The product is called Cazena and it allows you to compile a service stack in the cloud to handle a variety of use cases, all while optimizing the TCO of your data environment. Right now, Cazena has stacks available for the data mart, data lake, and data science sandbox as a service. I find the company’s approach fascinating from a productized analytics standpoint. I’ve been chronicling the rise of #ProductizedAnalytics for a while now, (See explanatory graphic.) I think the movement towards these products are indicative of enterprises and tech innovators recognizing that not every analytic solution has to be custom-made – there’s room for Dunkin’ Donuts, just as much as there is for Per SE. (See “Productized Analytics: Why 100 Singles Are Better Than A Grand Slam”)

As I’ve written before, a fully enterprise-grade productized analytic offering allows you to take an analytic stack and apply it to a particular domain. To do this, the product must be able to handle a variety of sources of data, have an ingestion process, a generic data platform, analytic or application specific data objects, recommended models and analytics, reports and visualizations, and data exports and APIs. My theory is that productized analytics require analytic stacks that offer these capabilities to users – a great example is what Salesforce is doing with artificial intelligence in its productized analytic solution.

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