Koverse, an Accumulo based platform with easy to use indexing, analytics and security built in.
The Koverse Intelligent Solutions Platform enables organizations to design and build scalable and secure data-driven solutions in a high-availability/high-performance Accumulo based environment.
For DEVELOPERS AND ENGINEERS
Koverse provides a unified access layer to all data-enabled functionality. It enables developers to efficiently write, read, index, secure and build applications that deliver perfectly-suited solutions.
With Koverse’s high-level API, teams gain remarkable efficiency in their development efforts. Koverse integrates all of the components of the stack. Rather than working with individual storage systems and individual components, Koverse provides a uniform way of working with data — architecting data flows and integration with any other systems.
Via Koverse’s REST API, SDK, and user interface, Koverse provides a level of interaction with data that does not exist with any other technology. It allows developers, data engineers and data scientists to work together on the same platform.
Koverse’s Intelligent Solutions Platform provides the convenience of true abstraction so you don’t need deep expertise in complex data infrastructure. Koverse technology takes care of the unpleasant work, letting developers accomplish high-level activities faster..
For DATA SCIENTISTS
With Koverse’s cell-level security and access controls, your organization can now manage all data in one place and expose precisely the data sets required to inject intelligence into enterprise solutions.
Koverse’s Solutions Platform takes the hassles out of data wrangling and accelerates your most valuable work. Koverse provides a centralized, uniform source for any and all data, eliminating the need to hunt for data in disparates silos.
Data scientists no longer have to explicitly specify schemas. Koverse automatically delivers data sets with the schemas and formats that industry-standard tools expect. Whether using Spark DataFrames, R DataFrames, Spark RDDs, Zeppelin, or Jupyter Notebooks, data scientists can continue to use the tools they’re accustomed to using.