Data warehouse

Reporting

The data in the data warehouse must be available to the organisation's staff if the data warehouse is to be useful. There are a very large number of software applications that perform this function, or reporting can be custom-developed. Examples of types of reporting tools include:

* Business intelligence tools: These are software applications that simplify the process of development and production of business reports based on data warehouse data.

* Executive information systems (known more widely as Dashboard (business): These are software applications that are used to display complex business metrics and information in a graphical way to allow rapid understanding.

* OLAP Tools: OLAP tools form data into logical multi-dimensional structures and allow users to select which dimensions to view data by.

* Data Mining: Data mining tools are software that allow users to perform detailed mathematical and statistical calculations on detailed data warehouse data to detect trends, identify patterns and analyse data.

Metadata

Metadata, or "data about data", is used not only to inform operators and users of the data warehouse about its status and the information held within the data warehouse, but also as a means of integration of incoming data and a tool to update and refine the underlying DW model.

Examples of data warehouse metadata include table and column names, their detailed descriptions, their connection to business meaningful names, the most recent data load date, the business meaning of a data item and the number of users that are logged in currently.

Operations

Data warehouse operations is comprised of the processes of loading, manipulating and extracting data from the data warehouse. Operations also cover user management, security, capacity management and related functions

Optional Components

In addition, the following components exist in some data warehouses:

1. Dependent Data Marts: A dependent data mart is a physical database (either on the same hardware as the data warehouse or on a separate hardware platform) that receives all its information from the data warehouse. The purpose of a Data Mart is to provide a sub-set of the data warehouse's data for a specific purpose or to a specific sub-group of the organization.

A data mart is exactly like a data warehouse technically, but it serves a different business purpose: it either holds information for only part of a company (such as a division), or it holds a small selection of information for the entire company (to support extra analysis without slowing down the main system). In either case, however, it is not the organization's official repository, the way a data warehouse is.

2. Logical Data Marts: A logical data mart is a filtered view of the main data warehouse but does not physically exist as a separate data copy. This approach to data marts delivers the same benefits but has the additional advantages of not requiring additional (costly) disk space and it is always as current with data as the main data warehouse. The downside is that Logical Data Marts can have slower response times than physicalized ones.

3. Operational Data Store: An ODS is an integrated database of operational data. Its sources include legacy systems, and it contains current or near-term data. An ODS may contain 30 to 60 days of information, while a data warehouse typically contains years of data. ODSs are used in some data warehouse architectures to provide near-real-time reporting capability in the event that the Data Warehouse's loading time or architecture prevents it from being able to provide near-real-time reporting capability.

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