The document discusses various concepts related to database design and data warehousing. It describes how DBMS minimize problems like data redundancy, isolation, and inconsistency through techniques like normalization, indexing, and using data dictionaries. It then discusses data warehousing concepts like the need for data warehouses, their key characteristics of being subject-oriented, integrated, and time-variant. Common data warehouse architectures and components like the ETL process, OLAP, and decision support systems are also summarized.

1. Presented By: Deepali Raut- 54

3. Database Table

5. Data Dictionary

7. Non-Normalized Relation

8. Normalizing the Database

19. Evolution of Data warehouse

22. Typical Data Warehouse Architecture

24. Tools used in Data Warehousing Component Product used Purpose Reporting Crystal Reports Create presentation style reports with chart and graphs Querying Access 2000 Create complex ad-hoc queries against a variety of data sources OLAP Crystal Analysis Professional Access data cubes for designing views to pivot, filter and aggregate facts on pre-defined dimensions for specific subject areas Data Mining/Statistical Analysis SAS Statistical Analysis and Churn analysis

27. ETL Data Flow…

29. ETL Tools Informatica Power Center IBM Websphere DataStage(Formerly known as Ascential DataStage) SAP BusinessObjects Data Integrator IBM Cognos Data Manager (Formerly known as Cognos DecisionStream) Microsoft SQL Server Integration Services Oracle Data Integrator (Formerly known as Sunopsis Data Conductor) SAS Data Integration Studio Oracle Warehouse Builder AB Initio Information Builders Data Migrator Pentaho Pentaho Data Integration Embarcadero Technologies DT/Studio IKAN ETL4ALL IBM DB2 Warehouse Edition Pervasive Data Integrator ETL Solutions Ltd. Transformation Manager Group 1 Software (Sagent) DataFlow Sybase Data Integrated Suite ETL Talend Talend Open Studio Expressor Software Expressor Semantic Data Integration System Elixir Elixir Repertoire OpenSys CloverETL

32. OLTP vs. OLAP

33. Example: Invoice / Bill amount for a specific customer based on CAF Number (or) MDN needs to be found from a transactional system which is ADC Number of customers whose invoice / bill is greater than Rs.1000.00 for the past three months needs to have OLAP system which is DSS

38. Application Areas Industry Application Finance Credit Card Analysis Insurance Claims, Fraud Analysis Telecommunication Call record analysis Transport Logistics management Consumer goods promotion analysis Data Service providers Value added data Utilities Power usage analysis

40. Need of DSS ... at Different Level in An Organization

41. Case Study Telecom Industry

42. DSS Data warehouse Architecture

45. Sample Reports Delivery Circle Refund Pendency Report Total refund pendency JAN FEB MAR APR MAY JUNE AP 1 - - - 209 90 300 DL 4 - 2 - 112 23 141 GJ - - - - 411 123 534 KA 1 1 6 - 84 27 119 KL - - - - 31 10 41 MH 1 - 6 10 53 28 98 MP 12 13 53 - 82 - 160 MU - - - - 150 61 211 PB - 20 8 16 52 2 98 RJ - - - 2 9 4 15 TN 1 2 2 13 153 75 246 UP - 5 5 1 58 9 78 WB - - 1 - 97 64 162 Grand Total 20 41 83 42 1501 516 2203

46. Thank You

47. Generic two-level architecture Periodic extraction  data is not completely current in warehouse E T L BACK

48. Independent Data Mart BACK E T L Separate ETL for each independent data mart Data access complexity due to multiple data marts

49. Dependent data mart with operational data store BACK E T L Single ETL for enterprise data warehouse (EDW) Dependent data marts loaded from EDW

50. Logical data mart and @active data warehouse BACK BACK E T L Near real-time ETL for @active Data Warehouse Data marts are NOT separate databases, but logical views of the data warehouse  Easier to create new data marts ODS and data warehouse are one and the same