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You plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads:
-> A workload for data engineers who will use Python and SQL
-> A workload for jobs that will run notebooks that use Python, Scala, and SQL
-> A workload that data scientists will use to perform ad hoc analysis in Scala and R
The enterprise architecture team at your company identifies the following standards for Databricks environments:
-> The data engineers must share a cluster.
-> The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.
-> All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.
You need to create the Databricks clusters for the workloads.
Solution: You create a High Concurrency cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.
Does this meet the goal?
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A. B.B
No need for a High Concurrency cluster for each data scientist.
Standard clusters are recommended for a single user. Standard can run workloads developed in any language: Python, R, Scala, and SQL.
A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine-grained sharing for maximum resource utilization and minimum query latencies.
https://docs.azuredatabricks.net/clusters/configure.htmlThe proposed solution of creating a High Concurrency cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs does meet the specified requirements for the Databricks workspace.
Here is a breakdown of how the proposed solution meets each requirement:
Data engineers must share a cluster: The proposed solution includes a High Concurrency cluster for the data engineers, which is designed to be shared among multiple users. This cluster type is optimized for concurrent access and provides a cost-effective option for teams that need to collaborate on data engineering workloads.
The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster: The proposed solution includes a Standard cluster for jobs, which is intended to be used for batch processing and job scheduling. By using a request process, the data scientists and data engineers can submit packaged notebooks for deployment to the job cluster.
All data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity: The proposed solution includes a High Concurrency cluster for each data scientist. This cluster type is designed for individual use and can be set up to automatically terminate after a period of inactivity. By creating a separate cluster for each data scientist, the proposed solution ensures that they have their own isolated environment for ad hoc analysis.
Overall, the proposed solution meets the specified requirements for the Azure Databricks workspace and provides an optimized environment for each workload. Therefore, the answer is A. Yes.