Introduction To Msbi By Yasir


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A brief introduction on Data warehousing and implementing the data warehousing using MSBI

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Introduction To Msbi By Yasir

  1. 1. By - Shaik Yasir Ahmed
  2. 2. DataBase (DB) –A place where the collection of records will be maintained in a structured format so that Itcan be easily retrieved when ever required is known as a database .One of the most popularly used databasemodel is the relational model. It was developedby Edgar Codd in 1969.Example :How do you think the Organizations storetheir employee and customer information?they store it in a database.where do you think the website maintains thelogin information about their users?they store it in a database.
  3. 3. ERP– ERP, which is an abbreviation for Enterprise Resource Planning, is principally an integration of business management practices and modern technology. ERP is a business tool that management uses to operate the business day-in and day-out.OLTP–OLTP, which is an abbreviation for Online Transactionprocessing, handle real time transactions which inherentlyhave some special requirements. If your running a Bank, forinstance, you need to ensure that as people withdrawingmoney from ATM’S they are properly and efficiently updatingthe database also those transactions are properly effecting totheir Accounts.
  4. 4. Data, Data everywhere yet ... • I can’t find the data I need – data is scattered over the network • I can’t get the data I need • need an expert to get the data • I can’t understand the data I found • available data poorly documented • I can’t use the data I found • results are unexpected • data needs to be transformed from one form to other 6
  5. 5. What are the users saying...•Data should be integrated acrossthe enterprise•Summary data has a real value tothe organization•Historical data holds the key tounderstanding data over time•What-if capabilities are required 7
  6. 6. In What way I can Answer the above question with my OLTP system... Is Data Warehousing is the Solution ?? YES Can I Improve my business using Data warehousing ?? YES.. How ?? 8
  7. 7. Data warehouse helps any Business in Many Ways Let’s say A producer wants to know…. Which are our Which are our lowest/highest margin lowest/highest margin customers ? customers ? Who are my customers Who are my customers What is the most and what products and what products What is the most effective distribution are they buying? are they buying? effective distribution channel? channel?What product prom- What product prom- Which customers Which customers-otions have the biggest -otions have the biggest are most likely to go are most likely to goimpact on revenue? impact on revenue? to the competition ? to the competition ? What impact will What impact will new products/services new products/services have on revenue have on revenue and margins? and margins? 9
  8. 8. DWH – (Data Warehousing)It usually contains historical data derived from transaction data, but it can include datafrom other sources. It separates analysis workload from transaction workload and enablesan organization to consolidate data from several sources.Raugh kimball – In simplest terms Data Warehouse can bedefined as collection of Data marts. -Data marts : Subjective collection of Data.Bill Inmon – A data warehouse is a “subject-oriented,integrated, time variant and nonvolatile” collectionof data in support of management’s decision-makingprocess.”
  9. 9. OLAP – (Online Analytical Processing)The ability to analyze metrics in different dimensions such as time, geography, gender,product, etc. For example, sales for the company is up. What region is most responsible forthis increase? Which store in this region is most responsible for the increase? Whatparticular product category or categories contributed the most to the increase? Answeringthese types of questions in order means that you are performing an OLAP analysis.OLAP servers provides better performancefor accessing multidimensional data. Themost important mechanism in OLAP whichallows it to achieve such performance is theuse of aggregations.Aggregations are built from the fact table bychanging the granularity on specificdimensions and aggregating up data alongthese dimensions. OLAP systems gives analytical capabilitiesthat are not in SQL or are more difficult toobtain.
  10. 10. 1. OLTP (on-line transaction processing) 1. OLAP (on-line analytical processing)2. Day-to-day operations: purchasing, 2. Data analysis and decision makinginventory, banking, manufacturing, payroll,registration, accounting, etc.3. The tables are in the Normalized form. 3. The tables are in the De-Normalized form.4. We Called the Storage objects as 4. We Called the Storage objects asTables. i.e., All the masters and the Dimension and Facts. i.e., All the mastersTransactions are stored in the tables. Are dimension and the Transactions are Facts.5. For Designing OLTP we used data 5. For Designing OLAP we used modeling. Dimension modeling. OLAP is classified into two i.e., MOLAP & ROLAP
  11. 11. Normalized Tables De-Normalized Tables Product_Dim Product Prod_Id Prod_Id Prod_Name Prod_Name Base_Rate Base_Rate Category Cat_Name Cat_Id Cat_Id Cat_Desc Cat_Name Group_NameGroup Cat_Desc Group_DescGroup_Id Group_IdGroup_Name Topics Later We will CoverGroup_Desc 1. Types of Dimensions 2. Slowly changing Dimensions 3. Hierarchies
  12. 12. SalesOrderDetails SalesOrder_FactCust_Id Cust_Id ReferenceSalesPerson Prod_Id keys ofProd_Id Order_Date DimensionsOrder_Date Delivery_DateBooked_Date Unit_Price NumericDelivery_Date fields QtyUnit_Price called as Total_Amount Fact orQty Tax measureTaxCreated_By Qty*Unit_Price+Tax=Total Amount Usually calculate all the calculations before storing into OLAP
  13. 13. Prod_Dim Org_DimProd_Id Org_Id……… SalesOrder_Fact ……… Cust_Id Prod_Id Order_Date Delivery_Date Org_Id Unit_Price Time_DimCust_Dim Qty DateCust_Id Total_Amount Year……… Tax Month ……… STAR Schema
  14. 14. Product_Dim SalesOrder_FactProd_Id Cust_IdProd_Name Prod_IdBase_Rate Order_DateCat_Name Delivery_DateCat_Desc Unit_PriceGroup_Name QtyGroup_Desc Total_Amount Tax
  15. 15. 1. Dimensions will have only 1. Dimension will have arelation with the Fact. relation other than Fact. (De-(Normalized model) Normalized model)2. One to many or One to 2. Used for many to manyOne relation will Occur. relation.3. Performance is fast but 3. Performance is Low butrequired huge storage space. required Less storage space.
  16. 16. A single, complete andconsistent store of dataobtained from a variety ofdifferent sources madeavailable to end users in a whatthey can understand and use ina business context. [Barry Devlin] 18
  17. 17. Data Warehousing -- It is a process • Technique for assembling and managing data from various sources for the purpose of answering business questions. Thus making decisions that were not previous possible • A decision support database maintained separately from the organization’s operational database 19
  18. 18. Also Data Mining works with Warehouse Data Data Warehousing provides the Enterprise with a memoryData Mining provides the Enterprise with intelligence 20
  19. 19. Oracle 10g IBM DB2Base Product $ 25K $ 40K $ 25K
  20. 20. Tuning $3K Diagnostics $3K Partitioning Performance $10K Expert (included) $10KManageabilityBase Product $ 25K $ 40K 56K $ 25K 35K
  21. 21. DB2 OLAP $35K DB2 Warehouse OLAP $75K $20k Cube Views Mining $9.5K $20k BI Bundle $20kBusinessIntelligence (included)ManageabilityBase Product $ 25K $ 116K $ 56K $ $ 35K 154.5K
  22. 22. Data Guard $116K Recovery Expert $10kHigh AvailabilityBusinessIntelligence (included)ManageabilityBase Product $ 25K $ 116K 232K $ 154.5K 164.5K
  23. 23. $116K - $164.5K $232KMulti-coreHigh AvailabilityBusinessIntelligence (included)ManageabilityBase Product $ 25K $$464k- 232K $348k $$164.5K 329K
  24. 24. Data Reporting, OLAP, Analysis Data Mining Data Storage RepositoryData-Migration Middleware (Populations-Tools)OperationalData Sources
  25. 25. What happened? Why didit happen? What happened why and how?What willhappen? Number of Users Additional Benefit
  26. 26. OLTP O L A P ROLAP MOLAP Stage DB Optional CUBE SSAS Data Marts SSIS SSIS SSRS Integration Services Analysis Reporting Services Services
  27. 27. OLTP – Online Transaction ProcessingOLAP – Online Analytical ProcessingMOLAP – Multidimensional OLAPROLAP – Relational OLAPHOLAP – Hybrid OALPDimensions – De-normalized master tablesAttributes – Columns of DimensionsHierarchies – sequential order of attributesFacts (Measure group) – Transactions tables in DWHFact (Measures)Cubes – Multidimensional storage of DataKPI’s – Key performance indicatorDashboards – combination of reports,kpis,chartsData Marts – Subjective Collection of DataSCD’s – Slowly changing DimensionsPerspectives – Child Cube