Title: Characteristics of Data Warehouses
I. Introduction
A data warehouse is a central repository of integrated data from one or more disparate sources. It stores current as well as historical data and is used for reporting and data analysis. Understanding the characteristics of data warehouses is crucial for organizations aiming to make informed decisions based on their data.
II. Subject - Oriented
1、Business Focus
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- Data warehouses are designed around specific business subjects or areas of interest, such as sales, marketing, or finance. For example, in a sales - focused data warehouse, all relevant data related to sales transactions, customer orders, and product sales volumes are grouped together. This subject - orientation allows business users to easily access and analyze data relevant to their specific needs.
- It differs from operational systems which are more process - oriented. In an e - commerce operational system, the focus might be on the steps of processing an order, like inventory check, payment processing, and shipment. However, in the data warehouse, the focus could be on analyzing overall sales trends, customer purchasing patterns in relation to sales, etc.
2、Simplified Data Model
- The data model in a data warehouse is often simpler compared to the complex and normalized models in operational databases. It is designed in a way that is intuitive for business users. For instance, a star schema is commonly used in data warehouses. In a star schema for a sales data warehouse, there is a central fact table containing sales - related measures like sales amount and quantity sold. Surrounding this fact table are dimension tables such as customer, product, and time dimensions. This simple structure makes it easier for users to query and understand the relationships between different data elements.
III. Integrated
1、Data Source Consolidation
- Data warehouses integrate data from multiple sources. These sources can include different operational databases within an organization (such as ERP systems, CRM systems), flat files, and even external data sources like market research data. For example, a manufacturing company's data warehouse might integrate production data from its internal manufacturing management system, sales data from its CRM, and financial data from its accounting software.
- The integration process involves resolving data format differences. Different systems may store data in different formats. For instance, one system may use a date format of "MM/DD/YYYY" while another may use "YYYY - MM - DD". The data warehouse must standardize these formats during integration.
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2、Semantic Consistency
- In addition to format consistency, semantic consistency is also achieved. This means that the same data elements have the same meaning across different sources. For example, the term "customer" should refer to the same entity in both the sales and marketing data sources being integrated into the data warehouse. If in one source "customer" includes only active customers and in another it includes all customers (including inactive ones), this discrepancy needs to be resolved during integration.
IV. Time - Variant
1、Historical Data Storage
- Data warehouses store historical data, which is essential for trend analysis and decision - making. For example, a retail company can analyze sales data over the past few years to identify seasonal trends, peak sales periods, and long - term growth patterns. This historical data can also be used to compare current performance against past performance.
- The time - variant nature also allows for "slicing and dicing" of data over different time periods. Users can analyze data on a daily, monthly, quarterly, or yearly basis depending on their requirements.
2、Time - Stamped Data
- Each data entry in the data warehouse is typically time - stamped. This time - stamping enables accurate tracking of when a particular event occurred. For example, in a financial data warehouse, a transaction record will have a timestamp indicating when the transaction took place. This information is crucial for auditing purposes and for analyzing the sequence of events over time.
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V. Non - Volatile
1、Data Stability
- Once data is loaded into the data warehouse, it is relatively stable. Unlike in operational databases where data is constantly updated, inserted, and deleted as part of normal business operations, data warehouse data is mainly used for read - only operations for reporting and analysis. For example, once a sales transaction is recorded in the data warehouse, it is not typically modified directly.
- This non - volatility ensures the integrity of historical data and allows for consistent reporting. If data in the data warehouse was constantly changing in an unregulated way, it would be difficult to generate accurate historical reports and perform reliable trend analysis.
2、Backup and Recovery
- Since the data warehouse contains important historical and aggregated data, proper backup and recovery mechanisms are in place. In case of a system failure or data corruption, the data can be restored to a previous state. This is different from operational databases where the focus may be more on real - time recovery to ensure continuous business operations. In a data warehouse, the recovery is more about restoring the integrity of the data for accurate analysis.
In conclusion, the characteristics of data warehouses - being subject - oriented, integrated, time - variant, and non - volatile - make them a powerful tool for organizations to manage and analyze their data effectively. These characteristics enable businesses to gain valuable insights from their data, support strategic decision - making, and enhance overall competitiveness in the market.
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