Big-data can often benefit from the creation of summary/abstraction databases that provide enough detail for analytics and are linked to support deeper analysis where needed. This can speed access and reduce database cost.
When have you known me not to have suggestions! The starting point is to look at how summaries of the "main data" could be constructed to facilitate general queries. For example, health care information could be summarized/abstracted by condition/diagnosis, medication, etc. These abstractions are then linked (conceptually by key, again "diagnosis" is an example) to the main database. Now if somebody wants to look at the prevalance of conditions by location, date, etc. they can use the abstracted database. If they want to drill down, they can take their query results and use the "conditions" key to access the main database. By doing this they avoid spinning through detail records to analyze what's inherently summary data.
Tom, you said "What we should do when we're thinking about big data, meaning large data of any sort, is thinking about how that data could be abstracted in different ways to allow query and analytic access while reducing overall database usage." Sounds good. But how do we do that? Have you got any suggestions of how to start?
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