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Hi,
I'd like to get some advice on a dashboard implementation project. I have to create a dashboard for the enterprise and the main source is an Azure table with about 270M+ rows.
I created a SQL query to remove some unnecessary columns and to group by metrics. This reduces the data to about 45M+ rows.
I'm not sure if using this native SQL query would be best to ensure a scalable and reliable solution? Would I be able to implement incremental refresh? Would it be better to engage the database side to see if a view can be created and query the view instead? What do you recommend to ensure a scalable and reliable solution?
Solved! Go to Solution.
Hi @datapal04
I would highly recommend importing the data into your semantic model and then using incremental refresh to refresh only the latest data. This will ensure that your data is scalable and reliable and it is not relying on the underlying database system to serve the queries for the visuals.
Hi @datapal04
I would highly recommend importing the data into your semantic model and then using incremental refresh to refresh only the latest data. This will ensure that your data is scalable and reliable and it is not relying on the underlying database system to serve the queries for the visuals.
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