Saturday, November 19, 2022

User Experience — Databricks Vs Snowflake

 Cloud is the fuel that drives today’s digital organisations, where businesses pay only for those selective services or resources that they use over a period of time.

  • Snowflake clusters run within the snowflake plane, that’s the reason it can repurpose VMs instantaneously for its customers whereas in Databricks, clusters run in the customer plane (customer VPC or VNet), so acquiring a VM and starting the cluster takes time.
  • There’s a serverless option in Databricks also, which runs within no time. It’s a new offering where the VMs run in the Databricks plane. Databricks SQL warehouse has simplified cluster sizing similar to snowflake(t-shirt sizing).
  • Databricks compute is customer-managed and takes a long time to start-up unless you have EC2 nodes waiting in hot mode, which costs money. Snowflake compute is pretty much serverless and will start in most cases in less than 1 second.
  • Databricks compute will not auto-start, which means you have to leave the clusters running to be able to allow users to query DB data. Snowflake compute is fully automated and will auto start in less than a second when a query comes in without any manual effort.
Databricks is generally cheaper (cost for X performance), so it’s easier to keep a shared autoscaling cluster running in Databricks than in Snowflake. Same for warm-start pools. It’s not a 1:1 comparison with regard to cost over time for the same performance.
Last but not least, you can use any platform you feel is best for the job, but be aware of the maintenance, cost, and performance factors for anything you implement. Snowflake is especially essential for applications involving advanced analytics and data science. Data scientists primarily utilize R and Python to handle large datasets. Databricks provides a platform for integrated data science and advanced analysis, as well as secure connectivity for these domains.

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