Business Intelligence

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  • Business Intelligence:
  • Online Analytical Processing: OLAP is loosely defined set of principles that provide a dimensional framework for decision support. "The release of SQL Server 7.0 was a big turning point," Built in OLAP capabilities.
  • Data Warehouse: The conglomeration of an organization’s data warehouse staging and presentation areas, where operational data is specifically structured for query and analysis performance and ease-of-use.
  • Set of processes by which the operational source data is prepared for the data warehouse. The primary processes of the backroom data staging area of the data warehouse, prior to any presentation or querying. Consists of extracting operational data from a source application, transforming it, loading and indexing it, quality-assuring it, and publishing it.
  • The generic representation of a dimensional model in a relational database in which a fact table with a composite key is joined to a number of dimension tables, each with a single primary key.
  • A normalized dimension where a flat, single-table dimension is decomposed into a tree structure with potentially many nesting levels. In dimensional modeling, the fact tables in both a snowflake and star schema would be identical, but the dimensions in a snowflake are presented in third normal form, usually under the guise of space savings and maintainability. Although snow-flaking can be regarded as an embellishment to the dimensional model, snow-flaking generally compromises user understandability and browsing performance. Space savings typically are insignificant relative to the overall size of the data warehouse. Snow-flaked normalized dimension tables may exist in the staging are to facilitate dimension maintenance. Supported by MS Analysis Services when defining dimensions in cubes.
  • Fact tables are designed for growth, with new records being added weekly, daily or even hourly. Fact tables are never updated, unless a mistake is being corrected or a schema change is being made. Fact tables are never deleted, except when old records are being archived. A fact table has a foreign key for each of the dimension tables. The fact table’s primary key is a composite of all the foreign keys, Null values should not be allowed in these key fields. Fully additive – ApplesSold is fully additive because you can aggregate over all. It will aggregate to a correct value no matter what levels and dimensions are queried. They can be pre-aggregated when cubes are created. Semi additive – Apples in stock at the beginning of the day is semi additive. It will aggregate correctly with certain combinations of dimensions and levels, but fails to aggregate correctly with other combinations. Such as ApplesOnHand will not aggregate correctly across the time dimension. Non-additive – Average number of apples sold per sale. A non-additive measure will not aggregate across any dimension. Average values, percentages and most other measures based on mathematical formulas are non-additive.
  • Column – axis 0 , vertical filtering Row – axis 1, horizontal filtering Pages – axis 2, slice filtering Sections – axis 3, more than 1 slice filtering Chapters – axis 4, more than 1 slice with horizontal filtering ROLAP – Rational On-Line Analytical Processing, the data is stored in relational format and is slower for retrieval but easier for changing dimensions. MOLAP – Multidimensional On-Line Analytical Processing, the data is stored in a cube format and retrieval of data is fast and pre-aggregated. Takes up more space because the data is stored in relational and cube formats. HOLAP – Hybrid On-Line Analytical Processing, the data is stored both relationally and in cube format. Faster than ROLAP.
  • Business Intelligence

    1. 1. Business Intelligence Kim Tessereau Data Architect Quilogy, Inc.
    2. 2. Business Intelligence: Pronunciation: 'biz-n&s in-'te-l&-j&n(t)s a.k.a. BI. A generic term to describe leveraging the organization’s internal and external information assets for making better business decisions. OLAP ETL Fact Measure Dimension MDX Cube Star Schema Snowflaking Data Warehouse Data Mart
    3. 3. OLAP Online Analytical Processing <ul><li>Contrast to OLTP </li></ul><ul><li>An intuitive multidimensional data model makes it easy to select, navigate, and explore the data. </li></ul><ul><li>An analytical query language provides power to explore complex business data relationships. </li></ul><ul><li>Pre-calculation of frequently queried data enables very fast response time to ad hoc queries. </li></ul><ul><li>Microsoft® SQL Server™ 2000 Analysis Services is a robust OLAP tool </li></ul>
    4. 4. Data Warehouse <ul><li>Provides data for Business Analysis Process </li></ul><ul><li>Integrates data from heterogeneous source systems </li></ul><ul><li>Organizes data into subject-specific groups </li></ul><ul><li>Optimized for extraction and querying </li></ul>
    5. 5. Data Mart <ul><li>A logical and physical subset of the data warehouse’s presentation area </li></ul><ul><li>A flexible set of data, based on the most atomic data possible </li></ul><ul><li>Data Marts can be tied together using drill-across techniques </li></ul><ul><li>Data Marts can be connected to the data warehouse bus </li></ul><ul><li>The bus refers to the standard interface that allows separate Data Marts to </li></ul><ul><li>coexist usefully </li></ul><ul><li>A Data Mart can be a standalone stovepipe application </li></ul>
    6. 6. ETL <ul><li>E xtract, T ransform and L oad </li></ul><ul><li>Prepares source data for the data warehouse </li></ul><ul><li>Transforms data from relational form to a dimensional </li></ul><ul><li>Provides data quality assurance </li></ul><ul><li>Publishes data to the appropriate fact or dimension structures </li></ul>
    7. 7. <ul><li>Dimensional Data Modeling </li></ul><ul><li>De-normalized relational data modeling </li></ul><ul><li>Characterized by having one central fact table </li></ul><ul><li>Many surrounding dimension tables that de-normalize the descriptions of the fact table </li></ul><ul><li>One or more Star Schema’s to represent a subject-area within a data warehouse or data mart </li></ul>Star Schema
    8. 8. Example Star Schema
    9. 9. Snowflaking <ul><li>Defines hierarchs by using multiple dimension tables </li></ul><ul><li>More normalized than a single dimension table </li></ul><ul><li>Supported by MS Analysis Services </li></ul>
    10. 10. Fact <ul><li>The fact table is the heart of the star schema </li></ul><ul><li>Typically holds 95% of the space used by the star schema </li></ul><ul><li>Designed for growth </li></ul><ul><li>Fact tables are never updated , only appended to </li></ul><ul><li>Two types of fields, Keys and Measures </li></ul><ul><li>Fully additive, semi additive and non-additive measures </li></ul>Measure &
    11. 11. Example Fact Measure &
    12. 12. Cube <ul><li>The fundamental unit for data storage and retrieval in an OLAP system </li></ul><ul><li>Cubes are made up of Dimensions and Measures </li></ul><ul><li>MS OLAP cube can have up to 64 dimensions </li></ul><ul><li>Column, Rows, Pages, Sections & Chapters vs. Column & Rows </li></ul><ul><li>The source data of the cube is a star schema </li></ul><ul><li>Virtual cubes are very similar to a view in SQL Server </li></ul><ul><li>ROLAP, MOLAP and HOLAP are the storage behaviors for cubes </li></ul>
    13. 13. MDX <ul><li>M ulti- D imensional E x pression Language </li></ul><ul><li>Used to query OLAP cubes </li></ul><ul><li>Can be used to develop custom Business Intelligence applications </li></ul>
    14. 14. Demonstration Business Intelligence

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