Azure Databricks

Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

We would love to hear any feedback you have for Azure Databricks.
For more details about Azure Databricks, try our documentation page.

  1. Azure Databricks should have more granular level access permissions

    Currently, Azure Databricks Workspace provides only 4 options for access permissions.

    1. Workspace Access Control
    2. Cluster and Jobs Access Control
    3. Table Access Control
    4. Personal Access Tokens.

    These permissions give more access to user than requirement.

    Would it be possible to create more permissions under Access Control ?

    Specifically for below requirements

    Access to view data sources
    Access to view Databrick runs to check failures and their reasons
    Access to view data changes and deployment issues
    Access to troubleshoot data processing failures caused by Data issues, System errors in Databricks workspace

    10 votes
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  2. Launching Databricks WorkSpace from Azure Portal

    In order to launch the databricks workspace, the user needs to be an owner /contributor at the databricks resource level in azure portal, which is annoying for any enterprise users who are planning to roll out to larger audiences.

    Providing the direct workspace backend URL to the end user manually is not the ideal way , Since there are few now and will be 100's in the future.

    Permissions are set at the workspace and cluster level, When a user launches the workspace from the azure portal , whatever the api that is calling the databricks should validate the existing…

    7 votes
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  3. Cluster initialization time is too huge while databricks job run

    The simple job run even for a "print hello_world program" in databricks takes a minimum and fixed time lag of 10-12 seconds for spark initialization which is quite a significant latency. This time lag should be made as minimal as possible, there are certain other cloud providers like google etc. who are doing the same.

    4 votes
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  4. STOP the non-sense of making Resource Groups for these services if you really want us to use them!! Completely annoying.

    Totally insane. Databricks is the WORST offender of this, but Network Watcher does it as well. I won't allow RGs to be created unless they are NAMED and TAGGED according to OUR rules, so people cannot use this service. Period.

    6 votes
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    1 comment  ·  Strong Feedback  ·  Flag idea as inappropriate…  ·  Admin →

    Thanks for the valid suggestion. Your feedback is now open for the user community to upvote & comment on. This allows us to effectively prioritize your request against our existing feature backlog and also gives us insight into the potential impact of implementing the suggested feature.

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