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• Shared Query Engine: In this mode, the query engine not only performs queries but also handles semantic model processing. This dual role is named SHARED because the same process undertakes both activities.

• Dedicated Compute: In this mode, the semantic model is processed via dedicated service. In cloud-based deployment, the semantic model is processed using Kubernetes (K8S) cluster-based nodes while in ON PREM environment, models are processed on dedicated nodes.

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• AWS: For Kubernetes, Kyvos processes the semantic model using Amazon Elastic Kubernetes Service (Amazon EKS). You can select Query Engine or Kubernetes as a compute engine using no Spark model processing.
  For further details about deployment, see the Automated deployment for AWS via CloudFormation with Kyvos Native section.

• Azure: For Kubernetes, Kyvos processes the semantic model using Azure’s managed service AKS (Azure Kubernetes Service).

  • The Azure cluster is deployed via ARM templates. You can create a cluster without Spark or process the semantic model using Spark mode within ARM templates.
    For further details about deployment, see the Automated deployment on Azure with Kyvos Native section.

  • From Kyvos 2024.3 onwards, you can select the compute cluster as the Query engine or Kubernetes when deploying Kyvos through Azure Template Specs.

• GCP: For Kubernetes, Kyvos processes the semantic model using Google Cloud's managed service GKE (Google Kubernetes Engine). The GKE cluster is deployed through GCP Installation Files. Using the scripts, you can select a No-Spark-based cluster or process the semantic model using Spark Mode.
  To proceed with a no-spark-based deployment mode, you must use a Dataproc cluster (either new or existing)Optionally, for no-Spark deployments, you can either use new or existing Dataproc cluster.
  For further details about Kyvos deployment on GCP using the no-Spark model, see the following section:

  • Prerequisites to deploy Kyvos using Kubernetes

  • Creating Kyvos Resources for GCP using Script

• On Premises: For On-premises deployment, you can deploy using No Spark types: SHARED_QE and Compute Server. For further details about on premises deployment with no-Spark, see the Deploying no Hadoop no Spark.

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Points to know before using existing Kubernetes (K8s) cluster

1. Name spaces must be fixed for existing K8s cluster as kyvos-compute and kyvos-monitoring on AWS and GCP

2. Node pool of Kubernetes cluster must be for dedicated Kyvos use.

3. Even if a dedicated node pool is needed for Kyvos, currently, a single Kubernetes cluster can be used with any single Kyvos cluster. This means that one dedicated node pool of a K8s cluster cannot be used with one Kyvos cluster, and another dedicated node pool of the same K8s cluster cannot be used with another Kyvos cluster.

4. Node pool used for Kyvos must have single instance type used for pool.

5. Node pool with multiple instance type is not supported.

6. Currently, Azure is not supported for existing Kubernetes cluster in any of the three following cases: fresh

   1. Fresh automated deployment

   , fresh

   2. Fresh wizard- based deployment

   , and in an existing external compute based cluster.

7. Instance type of Node pool must be of 4 minimum cores & 16 GB memory requirement.

8. There must be required permissions to list Kubernetes clusters and their node pools; without these permissions, the input will be converted to a text input rather than a dropdown.

9. Node pool for GCP Kubernetes cluster must be of single zone. Multi-zone node pool is not supported.

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