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Anonymous's avatar
Anonymous
Not applicable
2 years ago
Solved

UpperCasing in Lakehouse tablenames

Hi,

I'm currently importing a file to a lakehouse, here the naming convention keeps the Uppercasing of the letters (I'm using Camelcasing). But, once im making transofmrations in my pypsark notebook (more exactly saveAsTable) then the uppercasing is removed, and I'm only seeing lowercasing of the previous name.  For example, FactTable becomes facttable, etc.
This is in Fabric that im talking about. I wonder is this here to stay or can I circumvent this somehow?

  • Anonymous's avatar
    Anonymous
    2 years ago

    Hi Anonymous ,

    At present there will not be any change with this.

    Appreciate if you could share the feedback on our feedback channel. Which would be open for the user community to upvote & comment on. This allows our product teams to effectively prioritize your request against our existing feature backlog and gives insight into the potential impact of implementing the suggested feature.

    Hope this helps. Please let me know if you have any further queries.

4 Replies

  • Anonymous's avatar
    Anonymous
    Not applicable

    Hi Anonymous ,

    Thanks for using Fabric Community.

    Yes, this is a limitation of the HIVE metastore. It stores the schema of a table in all lowercase.

    A table name can contain only lowercase alphanumeric characters and underscores and must start with a lowercase letter or underscore.

    Hope this will help. Please let us know if any further queries.

    • Anonymous's avatar
      Anonymous
      Not applicable

      Is this something that will change in the forseeable future? Or will this remain in fabric? 
      Thanks

      • Anonymous's avatar
        Anonymous
        Not applicable

        Hi Anonymous ,

        At present there will not be any change with this.

        Appreciate if you could share the feedback on our feedback channel. Which would be open for the user community to upvote & comment on. This allows our product teams to effectively prioritize your request against our existing feature backlog and gives insight into the potential impact of implementing the suggested feature.

        Hope this helps. Please let me know if you have any further queries.