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LM 7 Data Warehouse

Learn about the needs for data warehouses, levels, data marts, and key database concepts like big data, NoSQL, OLAP, and data mining. Understand the benefits and differences between OLTP and data warehouses. Explore three-tier architecture and the integration of data marts in the overall architecture.

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LM 7 Data Warehouse

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  1. LM 7Data Warehouse Dr. Lei Li

  2. Learning Objectives • Describe the needs for data warehouse • Describe the three levels of a data warehouse • Explain the independent and dependent data mart • Explain the basic concept of big data, NoSQL, OLAP, data visualization, and data mining.

  3. Data Warehouse • A relational database designed for query and analysis. • Subject-oriented: e.g. customers, patients, students, products • Integrated: consistent naming conventions, formats, encoding structures; from multiple data sources • Time-variant: can study trends and changes • Non-updatable: read-only, periodically refreshed • Data Mart • A data warehouse that is limited in scope

  4. Why Data Warehouse? • Integrated, company-wide view of high-quality information (from disparate databases) • Separation of operational and informational systems and data (for improved performance)

  5. Difference between OLTP & Data Warehouse

  6. Three Tier Data Warehouse Architecture Image source: http://www.tutorialspoint.com/dwh/dwh_architecture.htm

  7. Data Warehouse Architecture

  8. Data Warehouse Architecture with Data Mart

  9. Data Mart • Stand-alone data mart • Dependent data mart

  10. Stand-alone data mart Image source: http://www.zentut.com/data-warehouse/data-mart/

  11. Dependent Data Mart

  12. Other Database Concepts • Data mining – knowledge discovery in database • Big Data • No-SQL database

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