Data warehouse

From Wikipedia, the free encyclopaedia The basic architecture of a data warehouse In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise.

The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing[2] for additional operations to ensure data quality before it is used in the DW for reporting.

The typical extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.

The main source of the data is cleansed, transformed, catalogued, and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.

 Benefits

A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to:

Integrate data from multiple sources into a single database and data model. More congregation of data to single database so a single query engine can be used to present data in an ODS.

Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long-running, analysis queries in transaction processing databases.

Maintain data history, even if the source transaction systems do not.

Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger.

Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data.

Present the organization's information consistently.

Provide a single common data model for all data of interest regardless of the data's source.

Restructure the data so that it makes sense to the business users.

Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems.

Add value to operational business applications, notably customer relationship management (CRM) systems.

Make decision–support queries easier to write.

Organize and disambiguate repetitive data

Generic environment

The environment for data warehouses and marts includes the following:

Source systems that provide data to the warehouse or mart;

Data integration technology and processes that are needed to prepare the data for use;

Different architectures for storing data in an organization's data warehouse or data marts;

Different tools and applications for the variety of users;

Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes.

In regards to source systems listed above, R. Kelly Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases".

Regarding data integration, Rainer states, "It is necessary to extract data from source systems, transform them, and load them into a data mart or warehouse".

Rainer discusses storing data in an organization's data warehouse or data marts

Metadata are data about data. "IT personnel need information about data sources; database, table, and column names; refresh schedules; and data usage measures".

Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities. A key to this response is the effective and efficient use of data and information by analysts and managers.A "data warehouse" is a repository of historical data that are organized by subject to support decision makers in the organization.Once data are stored in a data mart or warehouse, they can be accessed.

Related systems (data mart, OLAP, OLTP, predictive analytics)

A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), hence they draw data from a limited number of sources such as sales, finance or marketing. Data marts are often built and controlled by a single department within an organization. The sources could be internal operational systems, a central data warehouse, or external data.Denormalization is the norm for data modeling techniques in this system. Given that data marts generally cover only a subset of the data contained in a data warehouse, they are often easier and faster to implement.

Difference between data warehouse and data mart
Attribute	Data warehouse	Data mart
Scope of the data	enterprise-wide	department-wide
Number of subject areas	multiple	single
How difficult to build	difficult	easy
How much time takes to build	more	less
Amount of memory	larger	limited

Types of data marts include dependent, independent, and hybrid data marts.[clarification needed]

Online analytical processing (OLAP) is characterized by a relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems, response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. OLAP databases store aggregated, historical data in multi-dimensional schemas (usually star schemas). OLAP systems typically have data latency of a few hours, as opposed to data marts, where latency is expected to be closer to one day. The OLAP approach is used to analyze multidimensional data from multiple sources and perspectives. The three basic operations in OLAP are : Roll-up (Consolidation), Drill-down and Slicing & Dicing.

Online transaction processing (OLTP) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). OLTP systems emphasize very fast query processing and maintaining data integrity in multi-access environments. For OLTP systems, effectiveness is measured by the number of transactions per second. OLTP databases contain detailed and current data. The schema used to store transactional databases is the entity model (usually 3NF).Normalization is the norm for data modeling techniques in this system.

Predictive analytics is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes. Predictive analysis is different from OLAP in that OLAP focuses on historical data analysis and is reactive in nature, while predictive analysis focuses on the future. These systems are also used for customer relationship management (CRM).