Build without pain
Everything built on the Koverse solutions platform is reusable.
Koverse enables you to design and build scalable and secure data-driven solutions in a high-availability/high-performance environment. It is designed with re-use in mind. The more you build, the faster each new initiative becomes.
You don't have to learn new tools.
Koverse technology works with the tools and technologies your team already uses most frequently, such as Java, Python, Spark, Map Reduce, REST, and others.
Koverse delivers analytics-ready data
Make analytics reusable and shareable.
Koverse provides an API for wrapping analytics and gives non-developers the ability to apply analytics to data sets. Data scientists can write an analytic once, then they and others can apply it to multiple and varied data sets, by configuring parameters in a UI. This democratizes valuable AI and machine learning techniques, making them available throughout your organization.
Accelerate getting your results into production.
Koverse allows machine learning and AI models and predictions that data scientists create to be put directly into production by adding security, scheduling, and reusability. With Koverse's high-performance indexing capability, machine intelligence can be embedded in enterprise processes and solutions throughout the organization and security accessed by thousands of users.
The Koverse solutions platform is fully integrated.
Step 1: Data Ingest, Tagging, Profiling and Indexing
Koverse can import data from a range of external sources - relational databases, remote file systems, streaming sources and more - and organize and secure this data into collections.
Upon ingest, Koverse indexes and securely tags every field within every record, securely storing the index and the original record. The result is that data ingested into Koverse is automatically searchable and pre-staged for standardized and regulated execution of analytical processing.
Step 2: Search
Koverse has a very robust and high-performance search capability that runs both on data ingested from external sources and data generated from internal analytical processing. Koverse search includes auto-suggest and runs across collections or in specific collections, across fields or within specific fields. Searches can be full text, ranges of numbers and dates, or geospatial regions. Range queries can span multiple dimensions such as space and time.
All indexes live in Apache Accumulo tables and are secured using the same column visibilities as are applied to original records. This provides massive scalability and assured access control.
Step 3: Internal Analytics
All analysis in Koverse is performed via transforms. Transforms consist of multi-stage Spark or MapReduce jobs and can be configured to take parameters so that, once an analytical algorithm is developed, it can be reused by non-developers simply via configuration in the Koverse UI.
Because Koverse provides a common data abstraction, transforms can be written once and applied to a variety of input collections. Koverse's transform API reduces the amount of boilerplate and configuration developers need to do compared to the off-the-shelf Apache Hadoop API, and all transform can be easily ported to the vanilla Apache Hadoop API if necessary.
Step 4: Koverse API and SDK
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Step 5: Cell-level Security Access Controls
The Koverse cell-level security model provides high security AND high performance in the same system. Koverse presents a complete suite of security controls: row-, column-, and dataset-level visibility and dataset-level role-based permissions. These access controls are applied consistently to data across the entire solutions platform with no impact on performance.