The Data Warehouse has been employed successfully across many different enterprise use cases for years, though Data Warehouses have also transformed, and must continue to if they want to keep up with the changing requirements of contemporary Enterprise Data Management. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources. Oracle breaks down Data Warehouse architectures into three simplified structures: basic, basic with a staging area, and basic with a staging area and data marts. With a basic structure, operational systems and flat files provide raw data and data are stored, along with metadata and summary data, where end users can access it for analysis, reporting and mining. Adding a staging area, which sits between the data sources and the warehouse, provides a separate place for data to be cleaned before entering the warehouse.
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When it comes to data warehouse designing, two of the most widely discussed approaches are the Inmon method and Kimball method. For years, people have debated over which one is better and more effective for businesses. Initiated by Ralph Kimball, this data warehouse concept follows a bottom-up approach to data warehouse architecture design in which data marts are formed first based on the business requirements.
The primary data sources are then evaluated, and an Extract, Transform and Load ETL tool is used to fetch different types of data formats from several sources and load it into a staging area. This model partitions data into fact table, which is numeric transactional data or dimension table, which is the reference information that supports facts. The star schema is the fundamental element of dimensional modeling in which a fact table is bounded by several dimensions.
Several star schemas can be constructed within a dimensional model to fulfill various reporting needs. To integrate data, Kimball suggested the idea of conformed data dimensions. It exists as a basic dimension table that is shared across different fact tables such as customer and product within a data warehouse or as the same dimension tables in various data marts. This guarantees that a single data item is used in a similar manner across all the facts.
An important designing tool in this approach is the enterprise bus matrix design by Kimball that vertically records the facts and horizontally records the conformed dimensions. This matrix displays how star schemas are constructed. It is used by business management teams as an input to prioritize which row of the Kimball matrix should be implemented first. Figure 1. Basic Kimball data warehousing architecture Source: Zentut. The model then creates a thorough logical model for every primary entity.
For instance, a logical model is constructed for product with all the attributes associated with that entity. This logical model could include ten diverse entities under product including all the details, such as business drivers, aspects, relationships, dependencies, and affiliations. The Inmon design approach uses the normalized form for building entity structure, avoiding data redundancy as much as possible. This results in clearly identifying business requirements and preventing any data update irregularities.
Next, the physical model is constructed, which follows the normalized structure. This model creates a single source of truth for the whole business. Data loading becomes less complex due to the normalized structure of the model. However, using this arrangement for querying is challenging as it includes numerous tables and links. This approach proposes constructing data marts separately for each division, such as finance, marketing sales, etc.
All the data entering the data warehouse is integrated. To ensure integrity and consistency across the enterprise, the data warehouse acts as a single data source for various data marts.
Figure 2. Basic Inmon data warehousing architecture Source: Stanford University. Both these approaches consider the data warehouse as a central repository that supports business reporting. Also, both methods use ETL for data loading. However, the main difference lies in modeling data and loading it in the data warehouse. The approach used for data warehouse construction influences the preliminary delivery time of the warehousing project and the capacity to put up with prospective variations in the ETL design.
Both the Inmon and the Kimball methods can be used to successfully design data warehouses. In fact, several enterprises use a blend of both these approaches called the hybrid model.
In the hybrid model, the Inmon method is used to form an integrated data warehouse. Whereas, the Kimball approach is followed to develop data marts using the star schema. The data warehouse designer has to choose a method depending on the various factors discussed in this article. This site uses functional cookies and external scripts to improve your experience. Which cookies and scripts are used and how they impact your visit is specified on the left.
You may change your settings at any time. Your choices will not impact your visit. NOTE: These settings will only apply to the browser and device you are currently using. Data Warehouse Concepts: Kimball vs. The Kimball Approach Initiated by Ralph Kimball, this data warehouse concept follows a bottom-up approach to data warehouse architecture design in which data marts are formed first based on the business requirements.
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The differences between Kimball and Inmon approach in designing data-warehouse
If you are working in data warehousing project or going to work on data warehouse project, the two most commonly designed methods are introduced by Ralph Kimball and Bill Inmon. Lets understand the basic difference between Ralph Kimball and Bill Inmon approaches towards data warehouse. Bill Inmon, has defined a data warehouse as a centralized repository for the entire enterprise. The top-down approach is designed using a normalized enterprise data model. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse.
Bill Inmon vs. Ralph Kimball
When it comes to data warehouse designing, two of the most widely discussed approaches are the Inmon method and Kimball method. For years, people have debated over which one is better and more effective for businesses. Initiated by Ralph Kimball, this data warehouse concept follows a bottom-up approach to data warehouse architecture design in which data marts are formed first based on the business requirements. The primary data sources are then evaluated, and an Extract, Transform and Load ETL tool is used to fetch different types of data formats from several sources and load it into a staging area.
Data Warehouse Concepts: Kimball vs. Inmon Approach
William H. Bill Inmon born is an American computer scientist , recognized by many as the father of the data warehouse. Inmon created the accepted definition of what a data warehouse is - a subject oriented, nonvolatile, integrated, time variant collection of data in support of management's decisions. Compared with the approach of the other pioneering architect of data warehousing, Ralph Kimball , Inmon's approach is often characterized as a top-down approach.