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  • Legacy data is historical dataThe working information of a staff member Working hours or time-off hours within the fiscal period, up to the current dateWorking Hours = Overtime, etc.Time-Off Hours = Vacation, Sick Leave, etc.
  • DataStage database, toolA tool set for designing, developing, and runnin.gapplications that populate one or more tables in a data warehouse
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    • 1. By: RAVI RANJAN DATA WAREHOUSE By: Ravi Ranjan
    • 2. DEFINITION Data Warehouse A collection of corporate information, derived directly from operational systems and some external data sources. Its specific purpose is to support business decisions, not business operations.
    • 3. THE PURPOSE OF DATA WAREHOUSING  Realize the value of data  Data / information is an asset  Methods to realize the value, (Reporting, Analysis, etc.)  Make better decisions  Turn data into information  Create competitive advantage  Methods to support the decision making process, (EIS, DSS, etc.)
    • 4. Data Warehouse Components• Staging Area • A preparatory repository where transaction data can be transformed for use in the data warehouse• Data Mart • Traditional dimensionally modeled set of dimension and fact tables • Per Kimball, a data warehouse is the union of a set of data marts• Operational Data Store (ODS) • Modeled to support near real-time reporting needs.
    • 5. DATA WAREHOUSE FUNCTIONALITYRelationalDatabases Optimized Loader ExtractionERPSystems Cleansing Data Warehouse Engine AnalyzePurchased QueryDataLegacyData Metadata Repository
    • 6. EVOLUTION ARCHITECTURE OF DATA WAREHOUSE GO TO Top-Down Architecture DIAGRAM GO TO Bottom-Up Architecture DIAGRAM GO TO Enterprise Data Mart Architecture DIAGRAM GO TO Data Stage/Data Mart Architecture DIAGRAM
    • 7. VERY LARGE DATA BASES WAREHOUSES ARE VERY LARGE DATABASES Terabytes -- 10^12 bytes: Wal-Mart -- 24 Terabytes Petabytes -- 10^15 bytes: Geographic Information Systems Exabytes -- 10^18 bytes: National Medical Records Zettabytes -- 10^21 bytes: Weather images Zottabytes -- 10^24 bytes: Intelligence Agency Videos
    • 8. COMPLEXITIES OF CREATING A DATA WAREHOUSE  Incomplete errors  Missing Fields  Records or Fields That, by Design, are not Being Recorded  Incorrecterrors  Wrong Calculations, Aggregations  Duplicate Records  Wrong Information Entered into Source System
    • 9. SUCCESS & FUTURE OF DATA WAREHOUSE The Data Warehouse has successfully supported the increased needs of the State over the past eight years. The need for growth continues however, as the desire for more integrated data increases. The Data Warehouse has software and tools in place to provide the functionality needed to support new enterprise Data Warehouse projects. The future capabilities of the Data Warehouse can be expanded to include other programs and agencies.
    • 10. DATA WAREHOUSE PITFALLS You are going to spend much time extracting, cleaning, and loading data Youare going to find problems with systems feeding the data warehouse Youwill find the need to store/validate data not being captured/validated by any existing system Large scale data warehousing can become an exercise in data homogenizing
    • 11. DATA WAREHOUSE PITFALLS… The time it takes to load the warehouse will expand to the amount of the time in the available window... and then some You are building a HIGH maintenance system You will fail if you concentrate on resource optimization to the neglect of project, data, and customer management issues and an understanding of what adds value to the customer
    • 12. BEST PRACTICES Complete requirements and design Prototyping is key to business understanding Utilizing proper aggregations and detailed data Training is an on-going process Build data integrity checks into your system.
    • 13. Top-Down Architecture BACK TO ARCHITECTURE
    • 14. Bottom-Up Architecture BACK TO ARCHITECTURE
    • 15. Enterprise Data Mart Architecture BACK TO ARCHITECTURE
    • 16. Data Stage/Data Mart Architecture BACK TO ARCHITECTURE