The latest entry into the effort to simplify large-scale analytics has come from MapR Technologies Inc. in the form of new templates designed to automate the implementation of three key emerging use cases for Spark. The release tops off a tremendous show of support for the open-source data crunching engine in recent days.

The biggest highlight of the lovefest was IBM’s decision to join the fray and commit over 3,500 of its engineers to helping improve the framework with homegrown enhancements, starting with an internally-developed library designed to expand the machine learning capabilities of Spark. That’s a focus that MapR shares in its new templates.

The first of the trio that the company is rolling out for its Hadoop distribution, which has been shipping with the open-source analytics framework since last year, employs automated pattern recognition to distinguish suspicious activity from regular traffic passing through the corporate network. The distilled insights are then made accessible through a complementary search tool.

Also included in the package is tooling and configuration guidance along with professional services aimed at providing help with the implementation process, components that the template shares with the other two. The difference is that the security functionality is replaced with features for processing time-dependant and genome data, respectively.

The three new additions join existing workflows for large-scale data warehousing and automated recommendation, a lineup that encompasses much of the most common usage for Spark. As such, the templates should provide the majority of organizations with a quick path to getting a proof-of-concept up and running, but won’t go much further than that.

While MapR’s pre-build capabilities may greatly reduced the amount of manual coding involved there’s still a need for a professional – or more often, an entire team – with the specialized skills necessary to operationalize Spark in order to put it to use. That’s especially true given that the organizations likely to choose the engine over simpler pre-packaged alternatives have requirements that exceeds a one-size-fits-all template.

If Spark is ever to achieve mainstream adoption in the enterprise, MapR, IBM and the other contributors to the upstream project will need to address that challenge head on. The new workflows represent another important, albeit small, step in the right direction.

Photo via Neerav Bhat