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Data Warehouse Design

Data Warehouse Design. Xintao Wu University of North Carolina at Charlotte Nov 10, 2008. Organization. Concepts Data Warehousing Concepts (Ch1) Logical Design Logical design in data warehouse (Ch2) Physical Design Physical design in data warehouses (Ch3) Hardware and I/O considerations

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Data Warehouse Design

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  1. Data Warehouse Design Xintao Wu University of North Carolina at Charlotte Nov 10, 2008

  2. Organization • Concepts • Data Warehousing Concepts (Ch1) • Logical Design • Logical design in data warehouse (Ch2) • Physical Design • Physical design in data warehouses (Ch3) • Hardware and I/O considerations • Parallelism and partitioning in data warehouses • Indexes (Ch6) • Integrity constraints (Ch7) • Basic Materialized views (Ch8) • Advanced materialized views • Dimensions (Ch10)

  3. Organization • Managing DW environment • Overview of extraction, transformation, and loading • Extraction • Transportation • Loading and transformation • Maintaining the DW • Change data capture • SQLAccess advisor • DW performance • Query rewrite • Schema modeling techniques • SQL for aggregation in DW • SQL for analysis and reporting • SQL for modeling • OLAP and data mining • Using parallel execution

  4. What is DW

  5. Logical vs. physical design • In the logical design, you look at the logical relationships among the objects. • In the physical design, you look at the most effective way of storing and retrieving the objects as well as handling them from a transportation and backup/recovery perspective. • Your logical design should result in • a set of entities and attributes corresponding to fact tables and dimension tables • A model of operational data from your source into subject-oriented informaiton in your target data warehouse schema.

  6. Physical Design • Logical design can use pen/paper/oracle warehouse builder/oracle designer while physical design is the creation of database with SQL • Physical design decisions are mainly driven by query performance and database maaintenance aspects. • You need to create • Tablespaces • Tables and partitioned tables • Views • A view takes the output of a query and treats it as a table. Views do not require any space in the database • Integrity constraints • In OLTP, they prevent the insertion of invalid data while in DW, they are only used for query rewrite. • Dimensions • A schema object that defines hierarchical relationships between columns or column sets. • Indexes and partitioned indexes • Bitmap indexes vs. B-tree indexes. Bitmap indexes are efficient for set-oriented operations. • Materialized views • Query results that have been stored in advance .

  7. Partition and parallel execution • Range partitioning • Hash partitioning • List partitioning • Composite partitioning

  8. Bitmap index

  9. One dimension table columns joins one fact table

  10. extension

  11. Integrity constraints • Unique constraints • NOT NULL constraints • FOREIGN KEY constraints

  12. Basic materialized views

  13. Materialized views with aggregates

  14. Dimension

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