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This approach incurs high latency because of data movement, doesn’t scale as data volumes grow and burdens the application tier with the task of managing and maintaining analytical models.
And deep analytics on real-time transactions are next to impossible without a lot of heavy lifting.
The downside is that the threshold is very high for tables with a large number of rows which leads to updates occurring less frequently which can cause performance issues.
In order to trigger the auto update statistics feature in SQL Server, the number of updated rows should exceed 20% of the table rows.
If you turn on this trace flag, the new threshold to update the statistics will be lower for tables with a high number of rows.
Statistics are small objects that describe the data distribution for tables and indexes in a SQL Server database.
These statistics are used by the SQL Server Query Optimizer to determine the best plan to execute a query.
As a result, analytical applications can now be far simpler and need only query the database for analytic results.
Updating machine learning models, deploying new models, and monitoring their performance can now be done in the database without recompiling and redeploying applications.