Data Modeling and the DW

SQL, DATA WAREHOUSE, AND DATA MARTS-Module 4 SLP: Data Modeling and the DW
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Part 1
Creating SQL queries
SQL queries are the statements that allow users to identify specific data from a database table, and this then forms the result table. The result-sets represented in the result tables depict the information that is selected by using queries. Databases store information in one central location, while also allowing for information sharing. Since it is possible to recollect specific information in a database, queries help to identify the information needed. Queries may contain conditions, whereby one chooses fields from the specified table as well as comparison values and the conditional operators. Office.Org Base also provides a report that contains the related tables, while also allowing one to view various data tables.
SQL> SELECT* FROM STUDENTS;
SQL> SELECT* FROM COURSE;
SQL> SELECT STUDENT ID, NAME, STATE FROM STUDENTS
SQL> SELECT, ITM 440 COURSE FORM COURSE
Part II






Data Modeling and the DW
Data modeling
Data Modeling relates to the graphical representation of data created for the purposes of analysis and design. Data models include data warehouses, as they follow the same design process while designing the architecture for data warehousing. Data modeling tools make it easier to visualize the data, and various software use graphical capabilities to represent the data. The ER diagram is one of the data modelers that helps to visualize situations and dimensional modeling have improved capability to visualize more abstract concepts. Hence, the data models are guidelines to the implementation of the data warehouse. They are consolidated before implementation to improve the effectiveness of data warehouses.
Data modeling extends to business intelligence, with analytic operations necessary to highlight the use of query to organize data. Since the data models also rely on rules to govern the data, data modeling tools are essential to organizing the database. The first part is the logical design while the other part is the physical design. The top-down approach relies on understanding business requirements while the bottom-up approach relies on creating data models from databases which have no data model (Wang, 2008). The concept of normalizing and de-normalizing data is also important during data modeling, as they are a blueprint to the database requirements. There are also constraints, columns, tables and relationships that give a clearer picture depicting the ‘map’ of the database.
Data warehousing
Data warehouses support the decision-making in an organization, integrating data into a large repository, while allowing for knowledge extraction. However, one does not have the right tools it is difficult to extract the information. Data extraction, transformation uploading the data and the integrating data into the data warehouses are the main phases (Ania & Cuevas, 2012). Data developers and architects recognize that data warehousing is a valuable enterprise asset that meets proper management. The data warehouse provides potential value as a repository of data, but there is a need to first assess the models against the database system.
A data warehouse differs from the data mart which merely stores information for small subsets of an organization or specific product. A data warehouse supports the entire organization or large sections of an organization when data has been loaded it becomes necessary to cleanse and remove any duplicate records in the data warehouses, allowing users to make more refined data queries as they search for information. As such, data modeling the data warehouse requires that one gathers information about the attribute, relationship, while also normalizing the model. One also requires documenting the model using a modeling tool the data warehouse follows the same procedure. However, there is a difference the process of identifying data and building attributes or attributes, while the outcome of a warehouse depicts a physical data warehouse
Data modeling for data warehouse
The conceptual, legal and the physical data models represent the levels of modeling. While structuring the data warehouse, developers anticipate the results of a database, by using models to represent the data in a way that makes it easier to extract data. The conceptual data model depicts the relationship between entities, while specifying the attributes, but not the primary key. The logical data model then focuses on details about the data, and the physical data model is the most complex of the there. The physical data model then lets the designers and users understand how to implement the data model.
Data warehousing is increasingly utilized to provide better solutions when organizations need better...