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Kyvos' cloud-native architecture is designed for cost-effective analytics with speed at scale. With built-in elasticity, the Kyvos BI Servers and Query Engines can scale up and down seamlessly to balance loads, ensure optimal utilization of resources, and deliver on SLAs. The separation of storage (Smart Aggregates) and compute (Query and Compute Engine) ensures enterprise governance and no data movement.
Kyvos builds processes an OLAP-based BI acceleration layer directly on AWS (Amazon Web Services) that consists of two main components: BI Servers and Query Engines (QE). The Kyvos BI server is deployed on a standalone Elastic Compute Cloud (EC2) instance. Query engines are also deployed on standalone EC2 instances. They can be configured to increase or decrease depending on the load.
Once the semantic models are builtprocessed, they are stored in S3 for persistent storage. To achieve high performance, Kyvos replicates the cuboids and their metadata on shared storage using the local disks of the query engines. This helps deliver higher performance as compared to querying cubes semantic models directly on S3.
The auto-scaling feature of EMR (Elastic Map Reduce) enables Kyvos to scale up and down on AWS at the time of semantic model processing using the Amazon EMR service.
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