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Mondrian and OLAP Overview

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Attached is an overview of Mondrian and OLAP, first presented at the RTP Pentaho User Group Q1 2012 Meetup.

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Mondrian and OLAP Overview

  1. 1. Mondrian and OLAP Frontends RTP Pentaho User Group Q1 2012 Meetup
  2. 2. Pentaho Overview <ul><li>BI Server (Frontend for tools)
  3. 3. Report Designer (canned report designer)
  4. 4. Mondrian (Schema workbench, aggregate designer)
  5. 5. Data Integrator
  6. 6. Other ad-hoc tools (reporting)
  7. 7. Weka (predictive analytics) </li></ul>
  8. 8. Enterprise Extras <ul><li>Analyzer
  9. 9. Interactive Reporting
  10. 10. Dashboard Designer
  11. 11. Data Integration Scheduler
  12. 12. Support </li></ul>
  13. 14. OLAP? <ul><li>On-line Analytical Processing </li></ul><ul><ul><li>Designed for Analytics, not transactions </li></ul></ul><ul><li>ROLAP (Mondrian) </li></ul><ul><ul><li>Relational OLAP </li></ul></ul><ul><li>MOLAP (Palo) </li></ul><ul><ul><li>Multidimensional OLAP </li></ul></ul>
  14. 15. ROLAP <ul><li>Benefits </li></ul><ul><ul><li>Data is stored in a Kimball-style star schema </li><ul><li>Usable by all other tools (reporting, dashboards, etc.) </li></ul><li>“Cube” is stored in memory </li></ul></ul><ul><li>Cons </li></ul><ul><ul><li>Performance while “cube” is being cached
  15. 16. Performance depending on backend database </li></ul></ul>
  16. 17. MOLAP <ul><li>Benefits </li></ul><ul><ul><li>Data stored in multidimensional format
  17. 18. Usually highly compressed </li></ul></ul><ul><li>Cons </li></ul><ul><ul><li>Potentially long processing times to handle permutations
  18. 19. Higher cardinality (dimensions with millions of records) increases processing </li></ul></ul>
  19. 20. Mondrian Development Life-cycle
  20. 21. ROLAP Optimizations <ul><li>Columnar data stores </li></ul><ul><ul><li>Built for huge datasets in a conformed dimension format
  21. 22. Highly compresses and scales
  22. 23. Examples: LucidDB, Infobright, InfiniDB </li></ul></ul>
  23. 24. Mondrian Specific Optimizations <ul><li>Aggregate Designer </li></ul><ul><ul><li>Performs cost/benefit analysis on all permutations of data
  24. 25. Builds SQL queries that can be loaded into ETL or plugins (like with LucidDB) and run at set times </li></ul></ul><ul><li>Cons </li></ul><ul><ul><li>Have to refresh aggregate data as new data comes in – will get stale otherwise!
  25. 26. Time to refresh is dependent on data set </li></ul></ul>
  26. 27. MDX <ul><li>MultiDimensional Expressions
  27. 28. “SQL” for OLAP
  28. 29. Open standard developed by Microsoft </li></ul>
  29. 30. MDX Source: http://sqlblogcasts.com/blogs/drjohn/archive/2008/09/27/mdx-and-sql-combining-relational-and-multi-dimensional-data-into-one-query-result-set.aspx

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