Wednesday, August 01, 2007

Data capture vs data retrieval

One old concept of data management which is still valid today (and at the same time insufficiently adopted) is the architectural split between data capture vs data retrieval. It seems a bit artificial to split datastores in this way, but in larger architectures it makes absolute sense. This is not only because of the traditional reasons of tuning the retrieval database for performance (the old data warehouse concept), but mainly because of lots of practical reasons. Here are some:

  • Data capture is usually a complex process with steps for QC and validation, while retrieval is read-only. This leads to different data models, security models, etc.
  • Data capture is usually for just a limited number of users with various access rights, while data retrieval requires a focus on sharing
  • Data retrieval environments are focused on information retention over time
  • Data capture environments should only exist once for a data type while data retrieval environments can exist in multiple ways. Once data is created it can be shared or replicated instantly via messaging services

Having this concept in the back of the mind whilst architecting a data environment for an enterprise (so not for small systems!) is very useful and can simplify enterprise wide solutions.

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