Welcome to Koverse Support

Koverse is a software company that provides an Intelligent Solutions Platform for digital business.

Organizations that are currently, or aspire to be, using data solutions to enable strategic business objectives.

Koverse is a software company and we also provide professional services in some cases to help our customers build solutions on top of our software.

Koverse is headquartered in Seattle WA, with staff in Denver CO, Eugene OR, Washington, DC. and New York, NY.

Koverse is the only product that seamlessly integrates data ingest, analytical processing, cell-level security and high-performance search into a single platform.

You need Koverse if you want to develop data-driven solutions based on your unique expertise and data, to take to market or for internal use.

You can build a wide variety data-driven solutions on top of Koverse, including applications involving machine learning and AI. Koverse is particularly well-suited for solutions that require a large volume and/or variety of complex and sensitive data sets.

Koverse can load structured and unstructured data, including records from databases, a wide range of files, and streaming data, without ETL.

If you have a supported data type you can load a new data set in minutes, using only the user interface; no code or console commands required.

If you have a data format that the Koverse solutions platform does not parse out of the box, or your data is stored in a location that the Koverse platform does not connect to out of the box, you will need to use our API to build a custom parser or connector, which will plug into the Koverse platform. This typically takes about a day and requires development in Java or Scala.

Existing Transforms (analytics) that come out of the box, or have already been built and deployed to the Koverse solutions platform, can be configured in minutes using only the user interface; no coding or console commands required.

Custom Transforms (analytics) can be built and deployed in days or weeks, depending on their complexity, using the Koverse API. This requires development in Java, Scala, or Python.

Yes. See our Real-time Monitoring use case on the Solutions page of this website for more details.

Yes, you can build it yourself with your own team or our Global Services team can assist you.

Koverse is different from traditional BI tools and data warehouses in that it is designed to enable scalable data solutions for a range of non-traditional use cases that require a combination of precision search, AI and ML and stringent security controls. Furthermore, Koverse can handle large varieties of data without ETL or pre-definition of schemas. In many cases, Koverse can complement enterprise BI tools and data warehouse capabilities that may already be in use by an organization.

Koverse is unique in combining search with original record storage, analytics, and cell-level access control into one system, eliminating the need to duplicate data between a search and analysis systems. While Koverse can support many of the same queries provided by Elastic Search, Koverse goes beyond Elastic Search by automatically discovering data schemas giving users the option of indexing any and all data without having to declare indexing policies up front. Koverse also supports searching across multiple data sets at once, and the ability to find a search term in any field.

Koverse is a scalable, big data platform that runs on top of a curated set of open source components including Accumulo and Hadoop. Unlike a collection of loosely integrated components that engineers must compose into a usable application, Koverse gives users a unified, higher-level platform and API on which powerful applications can be rapidly developed. In addition, the Koverse solutions platform provides a powerful and tightly integrated way of working with data sets, making it vastly simpler to manage indexing and search, analytic monitoring and scheduling, and ingestion and access control that these open source components do not provide.

Koverse allows data engineers, analysts, and data scientists to use many of the most popular tools, while making common tasks simpler. For example, data scientists can use Koverse to pull in data sets that are automatically structured for rapid prototyping and analysis within interactive notebooks such as Jupyter and Zeppelin. Data engineers can take portions of code written to leverage the popular Spark API or SQL and package them into reusable components that non-developers can use to build sophisticated data processing pipelines. Analysts can use Koverse’s UI to explore data interactively, search for relevant subsets, and download them, or pull data into other tools such as Excel and Tableau to do manipulations on their desktop. And of course, all these methods of working with data are governed by Koverse’s access control features, which data owners can easily configure to comply with regulations and policy.

Koverse has robust role-based access controls. Users can be granted access to only limited aspects of the system independently including data processing, ingest, search and administration.

Koverse server and web app and possibly Hadoop, Accumulo and Zookeeper, if not already installed.

Koverse runs as an application on top of Apache Hadoop and Accumulo. It is easy to install if you already have a Cloudera or Hortonworks cluster running. If you do not already have a Hadoop cluster, and do not wish to stand one up yourself, Koverse can run your cluster as a managed service.

You can run Koverse on-site on your own hardware or in the cloud.

Koverse, like the Hadoop ecosystem, runs on Linux operating systems. Recommended operating systems are listed here.

Koverse is the only platform that uses Apache Accumulo as the sole storage system for all original records and indexes, thereby providing superior data security and scalability.

Koverse can be configured to index everything, or a subset of fields chosen by users. Typically indexing storage costs are comparable to original record storage. Many users take advantage of lower costs of disk storage to achieve a search capability beyond what previous systems were able to provide, and to more speculatively explore using analytics to transform raw data into highly valuable result sets that back powerful new applications for digitizing business.

Yes.