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Oracle: Fundamental Of DW

Oracle: Fundamental Of DW

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Oracle: Fundamental Of DW Oracle: Fundamental Of DW Presentation Transcript

  • 101 Introduction to Data Warehousing Fundamentals
  • Definition of a Data Warehouse • A data warehouse is an enterprise structured repository of subject-oriented, time-variant data used for information retrieval and decision support. The data warehouse stores atomic and summary data.
  • Typical Data Warehousing Process Phase I: STRATEGY Identify business requirements. Define objectives and purpose of DW. Phase II: DEFINITION Project scoping and planning: Using building block approach Phase III: ANALYSIS Information requirements are defined. Phase IV: DESIGN Database structures to hold base data and summaries are created. Translation mechanisms are designed. Phase V: BUILD AND DOCUMENT The warehouse is built and documentation is developed. Phase VI: POPULATE, TEST, AND TRAIN Iterative The warehouse is populated and tested. The users are trained on system and tools. Phase VII: DISCOVERY AND EVOLUTION The warehouse is monitored and adjustments are applied, or future extensions are planned.
  • Data Warehouse Compared to OLTP Property OLTP Data Warehouse Activities Processes Analysis Response Time Subseconds Seconds to hours to seconds Operations DML Primarily read-only Nature of Data Current Snapshots over time Data Organized By application By subject, time Size Small to large Large to very large Data Sources Operational, internal Operational, internal, external
  • Data Warehouse Compared with Data Mart Property Data Warehouse Data Mart Scope Enterprise Department Subjects Multiple Single-subject, line of business (LOB) Data Source Many Few Size (typical) See notes below See notes below Implementation Months to years Months Time
  • Independent Versus Dependent Marts Data Data Sources marts Sources marts Ware- house Independent Dependent
  • Independent Data Mart Operational systems Flat files Sales or marketing data mart External data
  • Dependent Data Mart Operational Data warehouse Data mart systems Flat files Marketing Marketing Sales Finance Sales Human Resources Finance External data
  • Purpose of an Enterprise Model Extract Transform/Load Publish Subscribe Federated data warehouse Flat files TL Dependent data marts Staging areas L Access layers Portal Transformations Operational B2C E RDBMS B2B External Enterprise model Clickstream Server log (atomic data) files Metadata repository
  • Extract, Transform, Load (ETL) Processes – Extract source data. – Load data into warehouse. – Transform/clean data. – Detect changes. – Index and summarize. – Refresh data. Programs Gateways Operational systems Tools Warehouse ETL
  • ETL Processes – Must result in data that is relevant, useful, high- quality, accurate, and accessible – Require a large proportion of warehouse development time and resources Relevant Clean up Useful Consolidate Quality Operational systems Restructure Warehouse Accurate ETL Accessible
  • Possible Reasons for ETL Failure – A missing source file – A system failure – Inadequate metadata – Poor mapping information – Inadequate storage planning – A source structural change – No contingency plan – Inadequate data validation
  • Typical Warehousing Development Tasks Define source metadata Source Define staging area metadata Map source to staging area to Deploy database structures staging Deploy mappings Extract data into staging tables Define enterprise model (warehouse) metadata Staging Map staging area to enterprise model to Deploy database structures warehouse Deploy mappings Extract data into the enterprise model Define data mart metadata (cubes, dimensions) Warehouse Map enterprise model to data marts to Deploy database structures data marts Deploy mappings Extract data into the data mart Refresh warehouse and data mart Administration Maintain warehouse and data mart
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