Schema on Write
Techopedia Explains Schema on WriteIn the early days of digital data handling, databases worked on a schema-on-write basis. That meant that data pieces had to be tailored to a template or plan at the time of storage. Then, when users went to retrieve the data, it was already in an easily manageable format.
What experts found as they created more innovative data-handling tools was that schema on write was, in many ways, too restrictive. Although it did a good job of enforcing consistency of data, it also led to the rejection of many different kinds of less structured data. Raw or unstructured data is data that does not fit the database template, is not in easily digestible chunks or cannot be formatted into things like currency, name or identification formats. Raw data may be more diverse and less homogeneous, which makes it less easy for a computing system to digest. The individual parts of raw data may be also less recognizable. But IT experts learned that this type of data was also valuable.
Part of the reason for the original schema-on-write convention was that earlier systems simply did not have the ability to handle any less-structured data. A simple input/output system could not handle the differences in syntax or any other deviations from a strict norm. However, as both hardware and software capability increased over the past three decades, systems emerged that could handle unstructured data fairly well and even do some of the formatting automatically that used to be done painstakingly by human hands (e.g., old punch card systems).
The newer schema-on-read method shows that unstructured data can be stored in the system and formatted or structured when retrieved. This has led to the partial obsolescence of the schema-on-write method, which used to be the main method for most computing and storage systems.
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