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Kyvos on AWS

Owned by NIDHI BHANDARI
Last updated: Jun 05, 2024 by DocReviewer
2 min read

Applies to: Kyvos Enterprise  Kyvos Cloud (SaaS on AWS) Kyvos AWS Marketplace

Kyvos Azure Marketplace   Kyvos GCP Marketplace Kyvos Single Node Installation (Kyvos SNI)

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 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 processed, 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 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.

  • Kyvos reads data from S3 and processes it using the EMR cluster. It launches a series of MapReduce or Spark jobs for semantic model processing.

  • At the time of Kyvos deployment, EMR is configured such that the cluster can scale in or scale out to use only the resources that are needed.

  • This ensures that only the required number of machines are running in the on-demand EMR cluster during the semantic model process.

Kyvos supports querying elasticity through scheduled and load-based scaling. Based on the expected loads, you can specify the day/time when resources need to scale up or down. This helps reduce costs during lean periods.

Architecture

Kyvos' modern architecture enables deep integration with AWS, as shown here.

Highlights

  • Elastic semantic model processing using EMR/Databricks

  • Leverages S3/ Delta Lake for semantic model storage

  • High-performance, elastic querying

  • No need to move data out of your storage platform

Related topics

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