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Alteryx Analytics Cloud Product Ideas

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Users have asked for the ability to create new versions of recipes so that they can collaborate safely. Also there is a need to keep an audit history of changes. Trifacta has recipe level history but that does not fulfil the whole use case of version control. 

When our users request for productionizing a request, we require that users share their flow with our Data Engineering and Data Operations team. Currently sharing with individuals is working, but you have to remember to put everyone's name in there. It would be nice if you could enter a GCP Group name that contains the users so they only have to remember one user.

Right now there is no place where team members can collectively create flows and share at one place. If given the option to share the Folders among different members just like we have for flows it will be lot easier. For Example: If there is a folder with 4 different flows, and I share the folder with my team mates they can edit and created new flows over there and can see all the 4 different flows already present. But if out of 4 flows if I share 2 flows with someone, they see the folder but they don't see the flows not shared with them.

There is no explicit sharing functionality for user macros between users or groups of users. Currently, the closest thing is users gain access to an owner’s macros within a flow once the flow is shared with them. It would be good to be able to share macros in the same way that users can share flows.

When I export a flow that contains a reference dataset, the name of the JSON file downloaded doesn't match the name of the flow that was exported. Instead it matches the name of the reference dataset inside the flow. I would like to change this so that a flow always keeps its original name when exported from Trifacta.

We would strongly like the ability to be able to edit datasets, created with custom SQL that have been shared with us. We think of Trifacta in part as a shared development space so if 1 users needs to make an update to a dataset but wasn't originally the owner - this slows down our workflow considerably.