Data Normalization and Storage
Normalization splits up data to avoid redundancy (duplication) by moving commonly repeating groups of data into new tables. Normalization therefore tends to increase the number of tables that need to be joined in order to perform a given query, but reduces the space required to hold the data and the number of places where it needs to be updated if the data changes.
From a space storage point of view, the dimensional tables are typically small compared to the fact tables. This often removes the storage space benefit of snowflaking the dimension tables, as compared with a star schema.
Some database developers compromise by creating an underlying snowflake schema with views built on top of it that perform many of the necessary joins to simulate a star schema. This provides the storage benefits achieved through the normalization of dimensions with the ease of querying that the star schema provides. The tradeoff is that requiring the server to perform the underlying joins automatically can result in a performance hit when querying as well as extra joins to tables that may not be necessary to fulfill certain queries.
Read more about this topic: Snowflake Schema
Famous quotes containing the words data and/or storage:
“To write it, it took three months; to conceive it three minutes; to collect the data in it—all my life.”
—F. Scott Fitzgerald (1896–1940)
“Many of our houses, both public and private, with their almost innumerable apartments, their huge halls and their cellars for the storage of wines and other munitions of peace, appear to me extravagantly large for their inhabitants. They are so vast and magnificent that the latter seem to be only vermin which infest them.”
—Henry David Thoreau (1817–1862)