The Importance Of A Data Computing Layer For Reporting

Buxing Jiang argues that there are reporting scenarios in which building a data computing layer is critical:

In previous articles, we mentioned that most reporting performance issues need to be addressed during the data preparation stage, but many scenarios can’t be handled within the data source. For example, parallel data retrieval should be performed outside of the data source because its purpose is to increase I/O performance. To achieve the controllable buffer, the buffer information needs to be written to an external storage device, which can’t be handled within a data source. The asynchronous data buffering and loading data by random page number in building a list report can’t be handled by a data source. Even for an associative query over multiple datasets that a data source can deal with, it would be necessary to get it done outside the data source when multiple databases or a non-database source is involved and when the database load needs to be reduced. Obviously, these scenarios that are not able to be handled within a data source also can’t be handled by a reporting tool.

I would be concerned about implementation details overwhelming the general value of a data computing layer.

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