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Figure 3-2. A fact table design for a time series to be stored in a relational database. The time, a
series ID, and a value are stored. Details of the series are stored in a dimension table.
In a star schema, one table stores most of the data with references to other tables known as
dimensions. A core design assumption is that the dimension tables are relatively small and
unchanging. In the time series fact table shown in Figure 3-2 , the only dimension being ref-
erenced is the one that gives the details about the time series themselves, including what
measured the value being stored. For instance, if our time series is coming from a factory
with pumps and other equipment, we might expect that several values would be measured on
each pump such as inlet and outlet pressures and temperatures, pump vibration in different
frequency bands, and pump temperature. Each of these measurements for each pump would
constitute a separate time series, and each time series would have information such as the
pump serial number, location, brand, model number, and so on stored in a dimension table.
A star schema design like this is actually used to store time series in some applications. We
can also use a design like this in most NoSQL databases as well. A star schema addresses the
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