Databricks on Azure VMs
April 05, 2022

Databricks on Azure VMs

Karan Dua | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User

Overall Satisfaction with Azure Virtual Machines

I used Azure Virtual Machines in my last organization for deploying out Machine Learning model and related workloads on virtual machines. Our requirement was to enable automated deployment of our compute engine - Databricks, our ML models, and Airflow workflows on scalable virtual machines and Azure Virtual Machines was our choice in the last organization I worked with.
  • Rapid Scalability
  • Variety of elastic storage options
  • Flexibility and control for app deployment
  • Regular Updates for security and feature upgrades
  • Fault tolerance
  • Native Integration with Databricks
  • Pricing can be a bit better
  • Compute types can be increased (AWS EC2 has more)
  • No Bare metal GPU instances as in OCI
  • Native Databricks deployment and upgradation was a breeze
  • Peace of mind with regards fault tolerance and automated backups
  • Native availability of Windows VMs made it easy to migrate Windows based on prem systems.
  • Amazon Elastic Compute Cloud (EC2)
Our main reason for selection of Azure Virtual Machines was easy availability of databricks and windows based VM natively. These features are not available on EC2.

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The VM deployment process is really simple in Azure Virtual Machines. But as I said earlier, compute types were a bit limited when I used it. In a few scenarios we had requirements for a Bare Metal GPU instance for high performance compute, but it wasn't available, so we had to look for alternatives.

Azure Virtual Machines Feature Ratings

Virtual machine automated provisioning
Management console
Live virtual machine backup
Live virtual machine migration
Hypervisor-level security