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DATA WAREHOUSING FUNDAMENTALS: A Comprehensive Guide for IT Professionals by Paulraj Ponniah

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11.7. CHAPTER SUMMARY

  • Slowly changing dimensions may be classified into three different types based on the nature of the changes. Type 1 relates to corrections, Type 2 to preservation of history, and Type 3 to soft revisions. Applying each type of revision to the data warehouse is different.

  • Large dimension tables such as customer or product need special considerations for applying optimizing techniques.

  • "Snowflaking" or creating a snowflake schema is a method of normalizing the STAR schema. Although some conditions justify the snowflake schema, it is generally not recommended.

  • Miscellaneous flags and textual data are thrown together in one table called a junk dimension table.

  • Aggregate or summary tables improve performance. Formulate a strategy for building aggregate tables.

  • A set of related STAR schemas make up a family of STARS. Examples are snapshot and transaction tables, core and custom tables, and tables supporting a value chain or a value circle. A family of STARS relies on conformed dimension tables and standardized fact tables.

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