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Data Warehouse Design
Xintao Wu
University of North Carolina at Charlotte
Nov 6, 2013
Organization
• Concepts
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Data Warehousing Concepts (Ch1)
• Logical Design
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Logical design in data warehouse (Ch2)
• Physical Design
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Physical design in data warehouses (Ch3)
Hardware and I/O considerations
Parallelism and partitioning in data warehouses
Indexes (Ch6)
Integrity constraints (Ch7)
Basic naterialized views (Ch8)
Advanced materialized views
Dimensions (Ch10)
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Organization
• Managing DW environment
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Overview of extraction, transformation, and loading
Extraction
Transportation
Loading and transformation
Maintaining the DW
Change data capture
SQLAccess advisor
• DW performance
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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
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What is DW
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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
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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.
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Physical Design
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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
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Tablespaces
Tables and partitioned tables
Views
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Integrity constraints
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A schema object that defines hierarchical relationships between columns or column sets.
Indexes and partitioned indexes
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In OLTP, they prevent the insertion of invalid data while in DW, they are only used for
query rewrite.
Dimensions
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A view takes the output of a query and treats it as a table. Views do not require any space
in the database
Bitmap indexes vs. B-tree indexes. Bitmap indexes are efficient for set-oriented operations.
Materialized views
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Query results that have been stored in advance .
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Partition and parallel execution
•Range partitioning
•Hash partitioning
•List partitioning
•Composite partitioning
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Bitmap index
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One dimension table columns joins
one fact table
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extension
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Integrity constraints
• Unique constraints
• NOT NULL constraints
• FOREIGN KEY constraints
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Basic materialized views
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Materialized views with aggregates
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Dimension
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