How to delete table in lakehouse fabric from azure synapse analytics/logic apps

[jovianaditya]

Can I drop/truncate the table in Microsoft Fabric Lakehouse from Azure Synapse Analytics or logic apps?

[Anonymous · Accepted solution]

Hi @jovianaditya 
Thanks for using Fabric Community.
Unfortunately, dropping or truncating tables directly from Azure Synapse Analytics or logic apps is not possible with Microsoft Fabric Lakehouse. But , you can access Notebooks within your Fabric workspace. Notebooks allow executing code like Python or SQL scripts, including DDL statements for dropping or truncating tables.
Hope this helps. Please let me know if you have any further questions.

[Anonymous · Accepted solution]

Hi @jovianaditya 
Thanks for using Fabric Community.
Unfortunately, dropping or truncating tables directly from Azure Synapse Analytics or logic apps is not possible with Microsoft Fabric Lakehouse. But , you can access Notebooks within your Fabric workspace. Notebooks allow executing code like Python or SQL scripts, including DDL statements for dropping or truncating tables.
Hope this helps. Please let me know if you have any further questions.

[Anonymous]

Hi @jovianaditya 
We haven’t heard from you on the last response and was just checking back to see if you have a resolution yet. Otherwise, will respond back with the more details and we will try to help.
Thanks

[jovianaditya]

Can Microsoft fabric pipeline trigger through az command? In synapse, I can trigger the pipeline using az pipeline. Can fabric pipeline trigger like that? I can trigger the notebook script to drop the table if it can be triggered like that.

[Anonymous · Accepted solution]

Hi @jovianaditya 
Unfortunately, Direct triggering of Microsoft Fabric pipelines using az commands is not yet possible. While Azure Synapse pipelines offer this functionality, Fabric currently only supports scheduled triggers.
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.