When starting from scratch, something new such as MapReduce technology is a relatively easy option to consider. After all, you have no major investments in SQL and there's not a lot of existing data that would need to be loaded in another database.
Looking to make it easier for any existing IT organizations to work with what is generally being referred to these days as "Big Data," Aster Data today is rolling out a series of templates for various types of applications within a variety of vertical industries.
Aster Data provides a massively parallel database called nCluster on which the the company has layered an implementation of MapReduce. To make that environment more appealing to many existing IT organizations, the Aster Data environment also supports SQL, giving customers the option of using either SQL or MapReduce as optional sets of tools for working within the Aster Data environment. Finally, the massively parallel database that anchors the system provides the necessary speed required to load and process large amounts of data.
According to Sharmila Mulligan, executive vice president of marketing, this essentially creates a huge data analytics server that IT organizations can use to provide the underlying horsepower that these types of applications require.
But providing the underlying engine isn't enough to ensure adoption. The company is now making available 40 analytic packages and over 1,000 MapReduce functions. The goal, says Mulligan, is to provide customers with an ability to start deriving value from their investments in the nCluster architecture in a matter of days.
Mulligan says it doesn't matter to Aster Data if customers want to use SQL or MapReduce or both. In many ways, both of those technologies are essentially means to an end. One may be less costly than the other, as proponents of the NoSQL movement are keen to point out. But as is often the case, it's more a matter of using the right tool for the right task than it is choosing one tool to the exclusion of all others.