Data drilling
Data drilling refers to any of various operations and transformations on tabular, relational, and multidimensional data. The term has widespread use in various contexts, but is primarily associated with specialized software designed specifically for data analysis.
Common data drilling operations
There are certain operations that are common to applications that allow data drilling. Among them are:Query operations:
- tabular query
- pivot query
Tabular query
Tabular query operations consist of standard operations on data tables.Among these operations are:
- search
- sort
- filter
- filter
- transform
Fred and Wilma table :
gender, fname, lname, home
male, fred, chopin, Poland
male, fred, flintstone, bedrock
male, fred, durst, usa
female, wilma, flintstone, bedrock
female, wilma, rudolph, usa
female, wilma, webb, usa
male, fred, johnson, usa
The preceding is an example of a simple flat file table formatted as comma-separated values. The table includes first name, last name, gender and home country for various people named fred or wilma. Although the example is formatted this way, it is important to emphasize that tabular query operations can be applied to any conceivable data type, regardless of the underlying formatting. The only requirement is that the data be readable by the software application in use.
Pivot query
A pivot query allows multiple representations of data according to different dimensions. This query type is similar to tabular query, except it also allows data to be represented in summary format, according to a flexible user-selected hierarchy. This class of data drilling operation is formally, known by different names, including crosstab query, pivot table, data pilot, selective hierarchy, intertwingularity and others.To illustrate the basics of pivot query operations, consider the Fred and Wilma table . A quick scan of the data reveals that the table has redundant information. This redundancy could be consolidated using an outline or a tree structure or in some other way. Moreover, once consolidated, the data could have many different alternate layouts.
Using a simple text outline as output, the following alternate layouts are all possible with a pivot query:
Summarize by gender :
female
flintstone, wilma
rudolph, wilma
webb, wilma
male
chopin, fred
flintstone, fred
durst, fred
johnson, fred
Summarize by home, lname :
bedrock
flintstone
fred
wilma
Poland
chopin
fred
usa
...