Data warehousing is a critical component of business intelligence and data analytics, and it involves the process of collecting, storing, and managing data from various sources to support decision-making. A data warehouse is a large, centralized repository designed for analytical reporting, as opposed to transaction processing. It is a system that gathers historical and current data from different operational databases, transforms and consolidates it, and makes it available for querying and analysis. The fundamental principles of data warehousing include:
1. Subject-Oriented: Data warehouses are designed to organize data around specific subjects, such as customers, products, sales, and finance. This is in contrast to operational databases, which are organized around business processes. For example, instead of storing sales transactions organized by date, as might be the case in an operational database, a data warehouse would be organized around the subject of sales, showing historical sales patterns and trends over time. The warehouse is designed to understand data in a way that makes it useful for decision makers in that subject area.
2. Integrated: Data warehouses integrate data from multiple sources. Data from different databases, files, and applications are brought together, cleaned, and standardized to ensure consistency and reliability. This involves resolving issues like different naming conventions, formats, and data types. For example, data from a CRM system, an ERP system, and a web analytics platform, each potentially with their own data formats and conventions, are combined in the data warehouse. These data sources would be transformed to ensure that customers are identified consistently, and that all the data is compatible for analysis. This ensures that all data across various systems is unified and ready for reporting and analytics.
3. Time-Variant: Data in a data warehouse is time-variant, which means that it stores historical data over time, allo....
Log in to view the answer
