Casey Karst
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Recent activity by Casey Karst-
Write credentials are GA. https://www.fivetran.com/blog/introducing-fivetran-managed-data-lake-write-credentialsWe do not support partitioning of tables associated with Fivetran syncs, but this doe...
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This feature was released via write credentials in MDLS. https://www.fivetran.com/blog/introducing-fivetran-managed-data-lake-write-credentials
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Hi Chris, We have made substantial improvements to our inline compaction ability without introducing any additional costs or overhead to users. If you are still experiencing what you consider subop...
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Official comment Thanks for the request! Fivetran Managed Data Lake (ADLS) writes the data as both Apache Iceberg and Delta so that the data can be consumed by downstream query engines. When the ADLS destination in...
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Official comment Each engine has it's own ways of configuring the ability to read from Fivetran Managed Data Lakes. While we do provide guidance for some external engines in the product it should not be considered ...
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Official comment Hi Germann, thanks for the feedback. This is something that we have added to our backlog but we do not have a current timeline for the release. -casey
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Official comment Hi Goncalo! Thanks for the feedback. We are about to release a secondary user credential which has write capability to our MDLS tables. This enables you to solve 2 quite easily. While you could th...
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Official comment Fivetran currently does not support S3 Tables as a Storage Provider. Managed Data Lakes provides a managed Data Lake offering on S3 buckets directly. Are there any features or capabilities in S3 ta...
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Official comment Thanks Megha for the request. We have been looking at improvements on the read side improvements including partitioning. We are making improvements in how we sort data during Fivetran Syncs that sh...
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Official comment Thanks Henrique for the request. In your thinking, would you want the time based partitioning based on the fivetran_sync time or a user defined time value in the dataset? For most of our workloads ...