Skip to main content

Bottom Up Approach in Data Warehouse


                                         Bottom-Up Approach






In a bottom-up approach, the goal is to deliver business value by deploying dimensional data marts as quickly as possible. Unlike the top-down approach, these data marts contain all the data—both atomic and summary—that users may want or need, now or in the future. Data is modeled in a star schema design to optimize usability and query performance. Each data mart builds on the next, reusing dimensions and facts so users can query across data marts, if desired, to obtain a single version of the truth as well as both summary and atomic data.


The “bottom-up” approach consciously tries to minimize back-office operations, preferring to focus an organization’s effort on developing dimensional designs that meet end-user requirements. The “bottom-up” staging area is non-persistent, and may simply stream flat files from source systems to data marts using the file transfer protocol. In most cases, dimensional data marts are logically stored within a single database. This approach minimizes data redundancy and makes it easier to extend existing dimensional models to accommodate new subject areas.







This approach is similar to the top-down approach but the emphasis is on the data rather than the business benefit. This is a “proof of concept” type of approach, therefore appealing to IT.

Both approaches advocate building a robust enterprise architecture that adapts easily to changing business needs and delivers a single version of the truth. In some cases, the differences are more semantic than substantive in nature. For example, both approaches collect data from source systems into a single data store, from which data marts are populated. But while “top-down” subscribers call this a data warehouse, “bottom-up” adherents often call this a “staging area.”

Comments

Popular posts from this blog

Contact Me

Do You have any queries ?                   If you are having any query or wishing to get any type of help related Datawarehouse, OBIEE, OBIA, OAC then please e-email on below. I will reply to your email within 24 hrs. If I didn’t reply to you within 24 Hrs., Please be patience, I must be busy in some work. kashif7222@gmail.com

Top 130 SQL Interview Questions And Answers

1. Display the dept information from department table.   Select   *   from   dept; 2. Display the details of all employees   Select * from emp; 3. Display the name and job for all employees    Select ename ,job from emp; 4. Display name and salary for all employees.   Select ename   , sal   from emp;   5. Display employee number and total salary   for each employee. Select empno, sal+comm from emp; 6. Display employee name and annual salary for all employees.   Select empno,empname,12*sal+nvl(comm,0) annualsal from emp; 7. Display the names of all employees who are working in department number 10   Select ename from emp where deptno=10; 8. Display the names of all employees working as   clerks and drawing a salary more than 3000   Select ename from emp where job=’clerk’and sal>3000; 9. Display employee number and names for employees who earn commission   Select empno,ename from emp where comm is not null and comm>0. 10

Informatica sample project

Informatica sample project - 1 CareFirst – Blue Cross Blue Shield, Maryland (April 2009 – Current) Senior ETL Developer/Lead Model Office DWH Implementation (April 2009 – Current) CareFirst Blue Cross Blue Shield is one of the leading health care insurance provided in Atlantic region of United States covering Maryland, Delaware and Washington DC. Model Office project was built to create data warehouse for multiple subject areas including Members, Claims, and Revenue etc. The project was to provide data into EDM and to third party vendor (Verisk) to develop cubes based on data provided into EDM. I was responsible for analyzing source systems data, designing and developing ETL mappings. I was also responsible for coordinating testing with analysts and users. Responsibilities: ·          Interacted with Data Modelers and Business Analysts to understand the requirements and the impact of the ETL on the business. ·          Understood the requirement and develope