Search results
Results from the WOW.Com Content Network
In the data warehouse practice of extract, transform, load (ETL), an early fact or early-arriving fact, [1] also known as late-arriving dimension or late-arriving data, [2] denotes the detection of a dimensional natural key during fact table source loading, prior to the assignment of a corresponding primary key or surrogate key in the dimension table.
Dimensions are the foundation of the fact table, and is where the data for the fact table is collected. Typically dimensions are nouns like date, store, inventory etc. These dimensions are where all the data is stored. For example, the date dimension could contain data such as year, month and weekday. Identify the facts
The SELECT clause sets the query axes as the Store Sales member of the Measures dimension, and the 2002 and 2003 members of the Date dimension. The FROM clause indicates that the data source is the Sales cube. The WHERE clause defines the "slicer axis" as the California member of the Store dimension.
The date dimension can include other attributes like the week of the year, or flags representing work days, holidays, etc. It could also include special rows representing: not known dates, or yet to be defined dates. The date dimension should be initialized with all the required dates, say the next 10 years of dates, or more if required, or ...
In computing, the star schema or star model is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. [1] The star schema consists of one or more fact tables referencing any number of dimension tables .
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension.
Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!
Data vault modeling was originally conceived by Dan Linstedt in the 1990s and was released in 2000 as a public domain modeling method. In a series of five articles in The Data Administration Newsletter the basic rules of the Data Vault method are expanded and explained.