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By Judith Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman

Custom and third-party applications offer an alternative method of sharing and examining big data sources. Although all the layers of the reference architecture are important in their own right, this layer is where most of the innovation and creativity is evident.

These applications are either horizontal, in that they address problems that are common across industries, or vertical, in that they are intended to help solve an industry-specific problem. Needless to say, you have many applications to choose from, and many more coming. It’s expected that categories of commercially available big data applications will grow as fast or faster than the adoption rate of the underlying technology.

The most prevalent categories as of this writing are log data applications (Splunk, Loggly), ad/media applications (Bluefin, DataXu), and marketing applications (Bloomreach, Myrrix). Solutions are also being developed for the healthcare industry, manufacturing, and transportation management, to name a few.

Like any other custom application development initiative, the creation of big data applications will require structure, standards, rigor, and well-defined APIs. Most business applications wanting to leverage big data will need to subscribe to APIs across the entire stack.

It may be necessary to process raw data from the low-level data stores and combine the raw data with synthesized output from the warehouses. As you might expect, the operative term is custom, and it creates a different type of pressure on the big data implementation.

Big data moves fast and changes in the blink of an eye, so software development teams need to be able to rapidly create applications germane to solving the business challenge of the moment.

Companies may need to think about creating development “tiger teams,” which rapidly respond to changes in the business environment by creating and deploying applications on demand. In fact, it may be more appropriate to think of these applications as “semicustom” because they involve more assembly than actual low-level coding.

Over time, certain types of applications will be created, in context, by the end user, who can assemble the solution from a palette of components. Needless to say, this is where the structure and standardization are most necessary. Software developers need to create consistent, standardized development environments and devise new development practices for rapid rollout of big data applications.