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Hello
I've run into an issue today where explicit schema mapping in data factory was causing an int32 to always come back as a double in "import schemas".
In azure data factory I could have resolved this by altering the .json definition of the pipeline, but the fabric json is read-only.
Being unable to change this meant that unix ticks to sql datetime conversion I needed to do failed. Fortunately I had a business rule to hand that meant I could coalesce the column when writing the file in pyspark before hand. However, if I did not have this business rule available the data type mapping being wrong/unchangeable would have stopped the analyst from being able to complete their Power BI piece.
Why is the json not updateable? When will the functionality be added in to make it updateable, for situations like this when the explicit mapping gets it wrong?
Hi @PhantomThread
Thanks for using Fabric Community.
At this time, we are reaching out to the internal team to get some help on this.
We will update you once we hear back from them.
Thanks
Hi @PhantomThread
Apologies for the delay in response. The internal team has confirmed that writeable is not supported yet. But it is on roadmap.
Incase if you have any specific requirment, 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 is helpful. Please let me know incase of further queries.
Hi @PhantomThread
We haven’t heard from you on the last response and was just checking back to see if you got some insights regarding your query. Otherwise, will respond back with the more details and we will try to help.
Thanks
You've mentioned that it's "on the roadmap".
Can it please be added to the official Fabric Roadmap at https://aka.ms/FabricRoadmap, so we can plan the implementation based on the official ETA for the rollout?
The current workarounds for the lack of this feature are extremely cumbersome and labour intensive, especially when it comes time to do deployments.
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