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Curious to get your thoughts on collecting stats in more of the old-fashioned way to collect and store stats for non-delta tables. Obviously, this would be less performant but I think some customers would be willing to pay for it. There are a lot of extended options for Analyze Table that could be used to collect proper metrics outside of delta. :) thoughts?
The text was updated successfully, but these errors were encountered:
I created a table analyzer notebook some time ago to give to customers in advance of tuning exercises in situations where we don't have hands on access to their workspace. Its very useful in helping drive the conversations around the potential benefit they would get by moving to delta (i.e showing them small file issues, data size disparity etc)
This notebook performs analysis and to break down tables / datasets by:
Another thought is to create views that instrument access to tables to help in optimization decisions.
How would you do this ?
You could have a dataframe pipeline that creates a temporary view - but as part of the pipeline, we write stats to a side table on logical primary keys accessed etc. Would have to experiment with this to see how to do this efficiently.
My general thinking behind this is based on experimenting with code coverage - the question is how would you do this for data use ? Haven't thought through this fully
Curious to get your thoughts on collecting stats in more of the old-fashioned way to collect and store stats for non-delta tables. Obviously, this would be less performant but I think some customers would be willing to pay for it. There are a lot of extended options for Analyze Table that could be used to collect proper metrics outside of delta. :) thoughts?
The text was updated successfully, but these errors were encountered: