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Kyvos installs directly on clusters located on the cloud and on-premise data lakes. It offers native support for all cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and the latest releases of Cloudera, Hortonworks, MapR, and Apache Hadoop.
Once deployed, Kyvos uses the compute capacity of modern data platforms to build multi-dimensional OLAP cubes on data at a massive scale. These cubes pre-aggregate data across multiple dimensions and measures and are stored in a distributed manner across the data infrastructure. As all the heavy-lifting is done in advance, these cubes provide instant response to queries enabling quick analysis of data. Kyvos maintains the native data security layer through the cubes at the storage layer itself, ensuring that users only see the data to which they have access.

Architectural diagram

BI acceleration layer

Kyvos builds a BI acceleration layer on the cloud and on-premise data lakes bridging the gap between your data and the business intelligence and analytics tools. This layer provides a consistent semantic model for business users, making it easy for them to visualize massive data. It allows them to see the dimensions and measures that are available to them and drag and drop them into their visualizations in an intuitive way using the BI tool of their choice. 

The next layer of the Kyvos architecture consists of the BI server cluster which enables aggregation on data at a massive scale. The BI servers are designed with active-active load balancing for scalable deployments to thousands of users across the enterprise. The built-in load balancer intelligently routes queries to the right server, in a manner that maximizes performance and capacity utilization. Multiple BI servers ensure high availability with minimal downtime. In the case of a BI server failure, another one is automatically promoted for use.
When the user fires a query, the BI tools connect to the Kyvos BI server using standard connectors. The BI server parses the query and routes it to the query engines which execute the query and return the results to the BI tool. The Kyvos query engine cluster is highly elastic. The number of active query engines can be easily increased or decreased to deal with varying loads, accommodate more users, or further reduce response times. Kyvos query engines are optimized to return most queries in less than a second.

Besides this, Kyvos has an intelligent, multi-level caching mechanism that ensures high performance based on query patterns and usage. It stores segments of the cubes most frequently queried and also caches the results of most frequent queries in high-performing storage.

Business intelligence and analytics tools

The topmost layer of the Kyvos architecture consists of BI and analytics tools that connect to Kyvos cubes using standard access mechanisms such as SQL and MDX, enabling users to access massive data instantly and interactively using their choice of BI tools. Kyvos supports all major BI tools, including Business Objects, Cognos, Excel, MicroStrategy, Power BI, Qlik, Spotfire, and Tableau. Kyvos cubes can also be accessed from data science engines like R and Python to discover data patterns. It supports REST and JAVA APIs that enable integration with custom enterprise applications.

Kyvos also has a native visualization engine with an intuitive drag-and-drop interface for self-service analysis.

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