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![What is a Data Warehouse?
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]](https://image.slidesharecdn.com/2f5cf49a-25aa-44d3-8eba-b6838cf718a8-160420181926/75/DWH_PROJECT-Compatibility-Mode-5-2048.jpg)
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DWH_PROJECT [Compatibility Mode]
- 1. PRESENTEDPRESENTEDPRESENTEDPRESENTED BYBYBYBY VASANTHKUMAR C 1DA12CS118 VEERABHADRAPPAKS 1DA12CS120 DWH Dr. AMBEDKAR INSTITUTE OF TECHNOLOGY
- 2. Loosely speaking, adata warehouse refers to a database that is maintained separately from an organization’s operational database practical interest in many applications such Decision Making in Companies by higher order database Administrators, Data Analysis…etc selection & dealing successfully with particular queries gives better results overall. DWH
- 3. DWH INTRODUCTION DATA WAREHOUSE vsOLTP DATA WAREHOUSE vs DATA MARTS DISCUSSION(Data Warehouse terminology) METHODOLGY ETL LIFE CYCLE FUTURE ENHANCEMENTS
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- 5. What is aData Warehouse? 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]
- 6. Definition of aData Warehouse “ An enterprise structured repository of subject-oriented, time-variant, historical data used for information retrieval and decision support. The data warehouse stores atomic and summary data.” DWH
- 7. 7 Warehouses are VeryLarge Databases 35% 30% 25% 20% 15% 10% 5% 0% 5GB 5-9GB 10-19GB 50-99GB 250-499GB 20-49GB 100-249GB 500GB-1TB Initial Projected 2Q96 Source: META Group, Inc. Respondents
- 8. Data Warehouse Properties Data Warehouse Integrated TimeVariantNon Volatile Subject Oriented DWH
- 9. Subject-Oriented Data is categorizedand stored by business subject rather than by application Supplier Customers Whole Sale Marketing Company Products Employees Shippers OLTP Applications Data Warehouse Subject DWH
- 10. Integrated OLTP Applications Products Order Detail Order DataWarehouse Data on a given subject is defined and stored once. Customer DWH
- 11. Time-Variant Data is storedas a series of snapshots, each representing a period of time Time Data Jan-97 January Feb-97 February Mar-97 March DWH
- 12. Nonvolatile Typically data inthe data warehouse is not updated or delelted. Insert Update Delete Read Read Operational Warehouse Load DWH
- 13. Changing Data Warehouse Database Firsttime load Refresh Refresh Refresh Operational Database DWH
- 14. Data Warehouse VersusOLTP Property Response Time Operations Nature of Data Data Organization Size Data Source Activities Operational Sub seconds to seconds DML 30-60 days Applications Small to large Operational, Internal Processes Data Warehouse Seconds to hours Snapshots over time Subject, time Large to very large Operational, Internal, External Analysis Primarily read only DWH
- 15. Data Warehouses Versus DataMarts Property Data Warehouse Data Mart Scope Enterprise Department Subject Multiple Single-subject, LOB Data Source Many Few Size(typical) 100 GB to>1 TB <100 GB Implementation time Months to years Months Data Warehouse Data Mart DWH
- 16. Dependent Data Mart Marketing Sales HumanResources (Employees) Shipper Categories Orders External Data Data Warehouse Operational Systems Flat Files Data Marts DWH
- 17. Data Warehouse Terminology Operational datastore (ODS) Stores tactical data from production systems that are subject-oriented and integrated to address operational needs Metadata Metadata DWH
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- 19. Methodology Ensures a successfuldata warehouse Encourages incremental development Provides a staged approach to an enterprisewide warehouse - Safe - Manageable - Proven - Recommended DWH
- 20. Modeling Warehouses differ fromoperational structures: - Analytical requirements - Subject orientation Data must map to subject oriented information: - Identify business subjects - Define relationships between subjects - Name the attributes of each subject Modeling is iterative Modeling tools are available DWH
- 21. 21 Components of theWarehouse Data Extraction and Loading The Warehouse Analyze and Query -- OLAP Tools Metadata Data Mining tools
- 22. Loading the Warehouse Cleaningthe data before it is loaded
- 23. Extraction, Transformation &Loading Purchase specialist tools, or develop programs Extraction-- select data using different methods Transformation--validate, clean, integrate, and time stamp data Loading--move data into the warehouse OLTP Databases ETL Tool Warehouse Database DWH
- 24. ETL Life Cycle Thetypical real-life ETL cycle consists of the following execution steps: 1. Cycle initiation 2. Build reference data 3. Extract (from sources) 4. Validate 5. Transform (clean, apply business rules, check for data integrity, create aggregates or disaggregates) DWH
- 25. DWH 6. Stage (loadinto staging tables, if used) 7. Audit reports (for example, on compliance with business rules. Also, in case of failure, helps to diagnose/repair) 8. Publish (to target tables) 9. Archive 10. Clean up
- 26. Data Access andReporting Tools that retrieve data for business analysis Imperatives - Ease of use - Intuitive - Metadata - Training More than one tool may be required Warehouse Database Charts Forecasting Drill-down DWH
- 27. 27 Snowflake schema Represent dimensionalhierarchy directly by normalizing tables. Easy to maintain and saves storage T i m e p r o d c u s t c i t y f a c t date, custno, prodno, cityname, ... r e g i o n
- 28. Oracle Warehouse Components Relational/ Multidimensional Text, image Spatial Web Audio video External data Operational data Relational tools OLAP tools Applications/Web Any DataAny Source Any Access DWH
- 29. Oracle Data MartSuite Data Modeling Oracle Data Mart Designer OLTP Engines OLTP Databases Data Extraction Oracle Data Mart Builder Ware- housing Engines Data Mart Database SQL*Plus Data Management Oracle Enterprise Manager Data Access & Analysis Discoverer & Oracle Reports DWH
- 30. Oracle Business Intelligence Tools CurrentTactical Strategic IS develops user’s Views Business users Analysis Reports Discover Express DWH
- 31. 31 Data Mining workswith Warehouse Data Data Warehousing provides the Enterprise with a memory Data Mining provides the Enterprise with intelligence
- 32. The Tool forEach Task Tool Reports Discover Express Production reporting Ad hoc query and analysis Advanced analysis Question What were sales by region last quarter? What is driving the increase in North American sales? Given the rapid increase in Web sales, what will total sales be for the rest of the year? Task DWH
- 33. 33 Reporting Tools Andyne Computing-- GQL Brio -- BrioQuery Business Objects -- Business Objects Cognos -- Impromptu Information Builders Inc. -- Focus for Windows Oracle -- Discoverer2000 Platinum Technology -- SQL*Assist, ProReports PowerSoft -- InfoMaker SAS Institute -- SAS/Assist Software AG -- Esperant Sterling Software -- VISION:Data
- 34. 34 Extraction and TransformationTools Carleton Corporation -- Passport Evolutionary Technologies Inc. -- Extract Informatica -- OpenBridge Informatica PowerCenter Information Builders Inc. -- EDA Copy Manager Platinum Technology -- InfoRefiner Prism Solutions -- Prism Warehouse Manager Red Brick Systems -- DecisionScape Formation
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- 36. DWH OLAP constructs inRDBMS: A relational database designed for OLTP will not serve well as a database for data analysis. Optimization techniques such as aggregating fact tables, partitioning fact tables, and denormalizing relation tables all provide significant improvements in performance. No Future Without Data Warehousing:
- 37. Summary following are coveredtopics: Identifying a common, broadly accepted definition of the data warehouse Distinguishing the differences between OLTP systems and analytical systems Defining some of the common data warehouse terminology Identifying some of the elements and processes in a data warehouse Identifying and positioning the Oracle Warehouse vision, products, and services DWH
- 38.