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No-Spark Semantic model processing through Kyvos Native Compute Engines

No-Spark Semantic model processing through Kyvos Native Compute Engines

Platform for Compute Type

Platform for Compute Type

You can choose Kyvos Native as a compute engine or External engines for semantic model processing.

 

Compute Type

 Platform

External

Kyvos Native

AWS

EMR / Databricks

SHARED QE or Dedicated Compute (K8S)

AZURE

Databricks

SHARED QE or Dedicated Compute (K8S)

GCP

Dataproc

SHARED QE or Dedicated Compute (K8S)

ON PREM

Hadoop

SHARED QE or Dedicated Compute

Deploy Kyvos through Kyvos Native Compute

Kyvos Native Compute operates independently of any external compute clusters when processing semantic models. It uses its proprietary Kyvos Analytical Store, which reduces costs, bolsters security, and removes the dependency on permissions.

Note

  • From Kyvos 2024.11 onwards, you can use the semantic model in a tabular way.

  • When you process a semantic model with no Spark, cuboids are stored at persistent storage. However, a copy of these cuboids is kept at the local storage (local disk).

  • 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.

Note

If you use Query Engines as a compute server:

  • For Load Based Scaling: Query Engines will be automatically started when the semantic model is processed.

  • For Schedule based scaling: Query Engines will start automatically when the semantic model is processed. However, if you have enabled schedule-based scaling, the Query Engines will not start automatically. In such cases, Kyvos recommends switching to load-based scaling.

  • 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.

You can change from an external compute cluster to Kyvos Native for processing semantic models through Kyvos Manager on the Compute Cluster page.

From Kyvos 2024.10 onwards, you can

  • Process semantic model with no-Spark using the Shared Query Engine and dedicated Kubernetes cluster on AWS Managed Services.

  • Resume failed or canceled semantic model process.

  • Run Process Test Data job on the semantic model.

Supported Platforms

 

Supported Environments

 

AWS

AZURE

GCP

ON PREM

Supported Native Types

Kyvos Enterprise

Marketplace

Managed Services

Enterprise

Marketplace

Enterprise

Marketplace

 

SHARED QE

Dedicated Compute (K8S)

  • 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.
    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:

  • 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.

Using existing Kubernetes cluster

Note

  • This applies only to AWS and GCP.

  • You can use the Kubernetes (K8s) enabled Kyvos cluster in the following cases:

    • Fresh Automated deployment

    • Fresh Wizard based deployment

    • Configuring K8s in an existing external compute-based cluster

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