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142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
142230 633685297550892500
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142230 633685297550892500

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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
  • Transcript

    • 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 Youare 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

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