Page Comparison

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AWS: You can use no Spark model processing to deploy Kyvos through Query Engine.
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 utilizing Spark Mode within ARM templates.
- Prerequisites to deploy Kyvos using Kubernetes
- Creating ARM Template for Automated Azure Deployment
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).
For more details about how to deploy Kyvos on GCP using the no-Spark model, see the following section:
- Prerequisites to deploy Kyvos using Kubernetes
- Creating Kyvos Resources for GCP using Script

Post deployment steps for all clouds (AWS, Azure and GCP)

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Add the kyvos.connection.readUsingCustomFS.jobs.internal=NONE property from the Kyvos connections page
Modify the values of the following properties in the advance properties of semantic model job:
- kyvos.process.compute.type=KYVOS_COMPUTE
- kyvos.build.aggregate.type=TABULAR
Set the kyvos.process.datastore.properties propertyon Hadoop Connection properties and set the value as SET disabled_optimizers = 'join_order';SET memory_limit='40GB';SET threads TO 1; Restart Kyvos services.

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