Introduction of Data warehousing and implementing the Data warehousing using MSBI

1. By - Shaik Yasir Ahmed

4. In What way a Data warehouse helps any Business Let’s say A producer wants to know…. Which are our lowest/highest margin customers ? Who are my customers and what products are they buying? Which customers are most likely to go to the competition ? What impact will new products/services have on revenue and margins? What product prom- -otions have the biggest impact on revenue? What is the most effective distribution channel?

6. A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context. [Barry Devlin]

8. A process of transforming data into information and making it available to users in a timely enough manner to make a difference [Forrester Research, April 1996] Data Information

10. Data Mining works with Warehouse Data Data Warehousing provides the Enterprise with a memory Data Mining provides the Enterprise with intelligence

14. Base Product $ 25K $ 40K $ 25K Oracle 10g IBM DB2

15. Base Product Manageability (included) $ 25K $ 40K $ 25K $ 56K $ 35K Tuning $3K Diagnostics $3K Partitioning $10K Performance Expert $10K

16. Base Product Manageability (included) $ 25K $ 35K $ 154.5K $ 56K $ 116K Business Intelligence OLAP $20k Mining $20k BI Bundle $20k DB2 OLAP $35K DB2 Warehouse $75K Cube Views $9.5K

17. Base Product Manageability (included) $ 25K $ 154.5K $ 164.5K $ 232K $ 116K Business Intelligence High Availability Data Guard $116K Recovery Expert $10k

18. Base Product Manageability (included) High Availability Business Intelligence Multi-core $348k - $464k $ 232K $ 25K $ 164.5K $ 329K $164.5K $116K - $232K

19. What happened? Why did it happen? What will happen? What happened why and how? Additional Benefit Number of Users

20. OLTP – Online Transaction Processing OLAP – Online Analytical Processing MOLAP – Multidimensional OLAP ROLAP – Relational OLAP HOLAP – Hybrid OALP Dimensions – De-normalized master tables Attributes – Columns of Dimensions Hierarchies – sequential order of attributes Facts (Measure group) – Transactions tables in DWH Fact (Measures) Cubes – Multidimensional storage of Data KPI’s – Key performance indicator Dashboards – combination of reports,kpis,charts Data Marts – Subjective Collection of Data SCD’s – Slowly changing Dimensions Perspectives – Child Cube

21. Operational Data Sources Data-Migration Middleware (Populations-Tools) Data Storage Repository Data Analysis Reporting, OLAP, Data Mining

22. Stage DB Optional ROLAP OLTP MOLAP O L A P SSIS Integration Services Analysis Services Reporting Services SSAS SSRS SSIS Data Marts CUBE

23. 1. OLTP (on-line transaction processing) 2. Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. 1. OLAP (on-line analytical processing) 2. Data analysis and decision making 3. The tables are in the Normalized form. 3. The tables are in the De-Normalized form. 5. For Designing OLTP we used data modeling. 5. For Designing OLTP we used Dimension modeling. OLAP is classified into two i.e., MOLAP & ROLAP 4. We Called the Storage objects as Tables. i.e., All the masters and the Transactions are stored in the tables. 4. We Called the Storage objects as Dimension and Facts. i.e., All the masters Are dimension and the Transactions are Facts.

24. Topics Later We will Cover 2. Slowly changing Dimensions 1. Types of Dimensions 3. Hierarchies Normalized Tables De-Normalized Tables Product Prod_Id Prod_Name Base_Rate Cat_Id Category Cat_Id Cat_Name Cat_Desc Group_Id Group Group_Id Group_Name Group_Desc Product_Dim Prod_Id Prod_Name Base_Rate Cat_Name Cat_Desc Group_Name Group_Desc

25. Qty*Unit_Price+Tax=Total Amount Usually calculate all the calculations before storing into OLAP Reference keys of Dimensions Numeric fields called as Fact or measure SalesOrder_Fact Cust_Id Prod_Id Order_Date Delivery_Date Unit_Price Qty Total_Amount Tax SalesOrderDetails Cust_Id SalesPerson Prod_Id Order_Date Booked_Date Delivery_Date Unit_Price Qty Tax Created_By

26. STAR Schema Prod_Dim Prod_Id ……… Cust_Dim Cust_Id ……… Time_Dim Date Year Month ……… Org_Dim Org_Id ……… SalesOrder_Fact Cust_Id Prod_Id Order_Date Delivery_Date Org_Id Unit_Price Qty Total_Amount Tax

27. Product_Dim Prod_Id Prod_Name Base_Rate Cat_Name Cat_Desc Group_Name Group_Desc SalesOrder_Fact Cust_Id Prod_Id Order_Date Delivery_Date Unit_Price Qty Total_Amount Tax

28. 1. Dimensions will have only relation with the Fact. (Normalized model) 1. Dimension will have a relation other than Fact. (De-Normalized model) 2. One to many or One to One relation will Occur. 2. Used for many to many relation. 3. Performance is fast but required huge storage space. 3. Performance is Low but required Less storage space.