Kyvos on Microsoft Azure

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 brings the power of multidimensional analytics to Azure. You can process a modern BI architecture on Azure with built-in elasticity and perform complex, multidimensional analytics on your cloud workloads with unmatched performance and unlimited scalability.

Kyvos consists of two main components: BI Servers and Query Engines. Kyvos BI servers are deployed on standalone Azure Virtual Machines (VMs), and the query engines are deployed on VMs in Virtual Machine Scale Sets. Query engines can be configured to increase or decrease depending on the load.

Auto-scaling enables Kyvos to scale up and down at the time of semantic model processing using Databricks on Azure.

Kyvos reads data from Azure Data Lake Storage (ADLS) and processes it using Databricks. It launches a series of MapReduce or Spark jobs for semantic model processing.
At the time of Kyvos deployment, leveraging the Databricks service, you can either provide a fixed number of worker nodes for the cluster or define the minimum and a maximum number of worker nodes. The cluster then scales in or out to use only the needed resources.
Databricks chooses the appropriate number of worker nodes required to run your job. This ensures that only the required number of machines are used during the semantic model processes.

Once the semantic models are processed, they are stored in ADLS GEN 2 for persistent storage. This helps deliver much higher performance as compared to querying directly from Blob storage.

Kyvos supports querying elasticity through scheduled 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.

Kyvos uses ADLS GEN2 shared storage to store and cache semantic model data. During scaling, when a new query engine is added, all that needs to happen is to point this new query engine to shared storage. This helps in quickly adding more capacity for querying. Similarly, when a query engine is taken off during a scale-down, another can quickly take over by pointing to the shared storage.

Architecture

Kyvos' modern architecture enables deep integration in the Azure ecosystem. As shown in the following figure, Kyvos also supports the Snowflake data warehouse on Azure.

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