Data warehouse

[2] Unlike databases, they are intended to be used by analysts and managers to help make organizational decisions.

[3] The data stored in the warehouse is uploaded from operational systems (such as marketing or sales).

Operational system designers generally follow Codd's 12 rules of database normalization to ensure data integrity.

Fully normalized database designs (that is, those satisfying all Codd rules) often result in information from a business transaction being stored in dozens to hundreds of tables.

The databases have very fast insert/update performance because only a small amount of data in those tables is affected by each transaction.

Online analytical processing (OLAP) is characterized by a low rate of transactions and complex queries that involve aggregations.

The OLAP approach is used to analyze multidimensional data from multiple sources and perspectives.

OLTP systems emphasize fast query processing and maintaining data integrity in multi-access environments.

Predictive analytics is about finding and quantifying hidden patterns in the data using complex mathematical models to prepare for different future outcomes, including demand for products, and make better decisions.

A data warehouse maintains a copy of information from the source transaction systems.

This architectural complexity provides the opportunity to: The concept of data warehousing dates back to the late 1980s[7] when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse".

In larger corporations, it was typical for multiple decision support environments to operate independently.

Additionally, with the publication of The IRM Imperative (Wiley & Sons, 1991) by James M. Kerr, the idea of managing and putting a dollar value on an organization's data resources and then reporting that value as an asset on a balance sheet became popular.

This concept served to promote further thinking of how a data warehouse could be developed and managed in a practical way within any enterprise.

Key developments in early years of data warehousing: A fact is a value or measurement in the system being managed.

For example, in a mobile telephone system, if a base transceiver station (BTS) receives 1,000 requests for traffic channel allocation, allocates for 820, and rejects the rest, it could report three facts to a management system: Raw facts are aggregated to higher levels in various dimensions to extract information more relevant to the service or business.

Facts are related to the organization's business processes and operational system, and dimensions are the context about them (Kimball, Ralph 2008).

Normalized relational database tables are grouped into subject areas (for example, customers, products and finance).

[24] In the bottom-up approach, data marts are first created to provide reporting and analytical capabilities for specific business processes.

The data warehouse bus architecture is primarily an implementation of "the bus", a collection of conformed dimensions and conformed facts, which are dimensions that are shared (in a specific way) between facts in two or more data marts.

Legacy systems feeding the warehouse often include customer relationship management and enterprise resource planning, generating large amounts of data.

A normal relational database, however, is not efficient for business intelligence reports where dimensional modelling is prevalent.

This modeling style is a hybrid design, consisting of the best practices from both third normal form and star schema.

The data vault model is not a true third normal form, and breaks some of its rules, but it is a top-down architecture with a bottom up design.

It is not geared to be end-user accessible, which, when built, still requires the use of a data mart or star schema-based release area for business purposes.

Consistencies include naming conventions, measurement of variables, encoding structures, physical attributes of data, and so forth.

Furthermore, avoiding the creation of a new database containing personal information can make it easier to comply with privacy regulations.

All data warehouses have multiple phases in which the requirements of the organization are modified and fine-tuned.