Multidimensional Database Design & Architecture


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Multidimensional Database Design & Architecture

  1. 1. Shan Hasan Abdul Samad (IS Advisor)
  2. 2.  Why Multidimensional Databases.  Comparison between Relational & Multidimensional Databases.  Multidimensional Database design & Architecture.  Dimensional Modeling.  Conclusion. ◦ Where to use multidimensional database.
  3. 3.  A multidimensional database (MDB) is a type of database that is optimized for data warehouse and online analytical processing (OLAP) applications.  Multidimensional data-base technology is a key factor in the interactive analysis of large amounts of data for decision-making purposes.
  4. 4.  Multi-dimensional databases are especially useful in sales and marketing applications that involve time series. Large volumes of sales and inventory data can be stored to ultimately be used for logistics and executive planning.
  5. 5.  Why Multidimensional Database ◦ Enables interactive analyses of large amounts of data for decision-making purposes. ◦ Differ from previous technologies by viewing data as multidimensional cubes, which have proven to be particularly well suited for data analyses. ◦ Rapidly process the data in the database so that answers can be generated quickly. ◦ A successful OLAP application provides "just-in-time" information for effective decision-making.
  6. 6.  Comparison Between Relational & Multidimensional Database ◦ Relational Database  The relational database model uses a two-dimensional structure of rows and columns to store data. Tables can be linked by common key values.  Accessing data from relational databases may require complex joins of many tables and is distinctly non-trivial for untrained end-users.
  7. 7.  Comparison Between Relational & Multidimensional Database ◦ Relational Database • To get the desired information from the data, organizations forced to hire IT professionals to structure such complex queries and also these complex queries takes huge time to return the results. • When writing queries such as INSERT, DELETE and UPDATE on tables, the consequences of getting it wrong are greatly increased when they are employed on a live system environment.
  8. 8.  Comparison Between Relational & Multidimensional Database ◦ Multidimensional Database  Enhance data presentation and navigation by intuitive spreadsheet like views that are difficult to generate in relation database.  Easy to maintain because data is stored in the same way as it is viewed, so no additional computational overhead is required.
  9. 9.  Comparison Between Relational & Multidimensional Database ◦ Multidimensional Database • Data analysis and decision making is much easier through multidimensional database as compare relational databases.
  10. 10.  Cubes ◦ Data cubes provide true multidimensionality. They generalize spreadsheets to any number of dimensions. ◦ Although the term “cube” implies 3 dimensions, a cube can have any number of dimensions. ◦ A collection of related cubes is commonly referred to as a multidimensional database.
  11. 11.  Dimensions and Members ◦ Dimension provides the means to slice and dice the data. It provides filtering and grouping of the data. ◦ Members are the individual components of a dimension. For example, Product A, Product B, and Product C might be members of the Product dimension. Each member has a unique name.
  12. 12.  Sparse & Dense Dimensions ◦ A sparse dimension is a dimension with a low percentage of available data positions filled. ◦ A dense dimension is a dimension with a high probability that one or more data points is occupied in every combination of dimensions.
  13. 13.  Data Storage ◦ Each data value is stored in a single cell in the database, in the form of multidimensional array.  Data Value ◦ The intersection of one member from one dimension with one member from each of the other dimensions represents a data value.
  14. 14.  Multidimensional Expression ◦ Multi-dimensional Expressions (MDX) is the most widely supported query language to date for reporting from multi-dimensional data stores. ◦ With MDX / mdXML, a robust set of functions makes accessing multi-dimensional data easier and more intuitive. ◦ MDX / mdXML does not have the data definition capabilities (DDL) that SQL has.
  15. 15.  Dimensional Modeling is a logical design technique that present the data in a standard, intuitive framework that allows for high- performance access.  In DM, a model of tables and relations is constituted with the purpose of optimizing decision support query performance in relational databases.
  16. 16.  Fact Table ◦ Fact table consists of the measurements and facts of the business process. ◦ A fact table typically has two types of columns: those that contains facts(numerical values) and those that are foreign key to dimension tables.
  17. 17.  Dimension Table ◦ The dimension table provides the detailed information about the attributes in the fact table. ◦ Fact tables connect to one or more dimension tables, but fact tables do not have direct relationships to one another.
  18. 18.  Star Scheme ◦ In the star schema design, a single object (the fact table) sits in the middle and is connected to other surrounding objects (dimension tables) like a star. ◦ A star schema has one dimension table for each dimension.
  19. 19. Star Scheme For Sales Cube
  20. 20.  Snowflake Scheme ◦ Snowflake schemas contain several dimension tables for each dimension. ◦ The main advantage of the snowflake schema is that it reduces the space required to hold the data and the number of places where it need to be updated if the data changes. ◦ The main disadvantage of the snowflake schema is that it increase the number of tables that need to join in order to perform the given query.
  21. 21. Snowflake scheme for Sales Cube
  22. 22.  Performance ◦ Multidimensional Database server typically contain indexes that provide direct access to the data, making MDD servers quicker when trying to solve a multidimensional business problem. ◦ MDDs deliver impressive query performance by pre- calculating or pre-consolidating transactional data rather than calculating on-the-fly.
  23. 23.  Data Volume & Scalability ◦ To fully pre-consolidate incoming data, MDDs require an enormous amount of overhead both in processing time and in storage. An input file of 200MB can easily expand to 5GB; obviously, a file of this size takes many minutes to load and consolidate. ◦ Some data is stored redundantly in the database . ◦ It is not suited for transaction processing as it takes time to store the calculated result in the database.
  24. 24.  Multidimensional Databases Torben Bach Pedersen Christian S. Jensen Department of Computer Science, Aalborg University.  Understanding Multidimensional Databases. et.htm?/docs/cd/E12032_01/doc/epm.921/html_esb_dbag/dinconc.htm  Data Mining & Analysis, LLC. Data Warehousing Service.  A Dimensional Modeling Manifesto by Ralph Kimball.  Multidimensional expressions for Analysis.  Comparison of Relational and Multidimensional database Structures. John Collins  Data Warehousing Architecture & major Components. Anupam Gupta. Anenues International Inc.  Dimensional Modeling and ER Modeling In The Data Warehouse by Joseph M. Firestone.  Online Analytical Processing (OLAP), Douglas S.Kerr.