Hadapt, Inc., which spawned from a joint research endeavor at Yale University, fleshed out details today about what it hopes to bring to the increasingly noisy sphere of big data analysis.

The company’s name provides a subtle hint about the nature of the offering (marrying the words Hadoop and Adapt). The startup plans to provide an analytical platform that will allow users to handle complex analytics workloads across what it calls a “cloud-optimized system.”

This morning the New Haven-based crew announced that it received its first round of financing to boost efforts behind their patent-pending technologies for high performance analytics on both structured and unstructured data. Hadapt’s technology is designed to tackle big data in both private and public cloud environments.

According to Hadapt, they are “adapting and expanding the Hadoop architecture to bring a more complete SQL interface, a patent-pending Adaptive Query Execution capability, and a hybrid storage engine to handle structured as well as unstructured data on a single platform.

In short, this means that they are another company seeking to bring the power of Hadoop to enterprise users by expanding the possibilities to analyze even more data types with help from a relational database.

The key piece of the company’s offering lies in the Adaptive Query Execution, which will be capable of dynamically load balancing queries in cloud environments and will allow jobs to be automatically split between relational database engines and Hadoop to maximize performance.

According to Borgman, there is nothing like the Adaptive Query Execution available today. He claims that during testing at Yale the group “was able to document query results 50 times faster than Hadoop with Hive and 600 times faster than Hadoop with HBase.”

On the cloud front, the company’s appeal is that this is native cloud-ware. In other words, as Borgoman stated, this was born for a virtualized environment thus, “unlike other second generation data warehousing companies, Hadapt has optimized the product architecture for big data and big analysis in virtualized environments.”

Hadapt is certainly not the only startup with an eye on the value of big data for enterprise use. Companies like Cloudera, for instance, which has at the heart of their branding the same  wish to “bring Hadoop to the masses” are no threat to Hadapt, according to Borgman during a recent interview. He notes that their offering is actually complementary to Cloudera, noting that “Cloudera focuses on tools and services to help ordinary IT staff run Hadoop whereas Hadapt is more fundamentally trying to make Hadoop better.”

Borgman is one of several co-founders behind the startup, which has its public “coming out” party today at the Structure event sponsored by GigaOm.

The founders of Habapt led a research team at Yale University that created and tested the initial prototype. Dr. Daniel Abadi, Chief Scientist and Co-founder is a database systems expert and behind the key Adaptive Analytical Platform at the heart of the company’s model. Abadi maintains a faculty position within Yale’s Computer Science department.

Also supporting the development and research for the Adaptive Analytical Platform is Kamil Bajda-Pawlikowski, who serves as chief software architect and co-founder in addition to his post at Yale. Dr. Avi Silberschatz, who is the team’s technical advisor (and another co-founder) was one of the visionaries behind the HadoopDB research that the company hopes to capitalize upon. Before joining the Yale computer science faculty, he was VP of the Information Sciences Research Center at Bell Labs.

The only co-founder who wasn’t involved in the research and testing directly was Justin Borgman, who serves as CEO comes from software development positions at MIT Lincoln Laboratory and Raytheon and more recently as head of product development at Covectra, a counterfeit prevention company. His Yale connection, however, extends to the university’s School of Management.

Hadapt hopes to tie up their software for formal release later in the year.