Mondrian and OLAP Overview

Mondrian and OLAP Overview

• 1.
  Mondrian and OLAPFrontends RTP Pentaho User Group Q1 2012 Meetup
• 2.
  Pentaho Overview BIServer (Frontend for tools)
• 3.
  Report Designer (cannedreport designer)
• 4.
  Mondrian (Schema workbench,aggregate designer)
• 5.
  Data Integrator
• 6.
  Other ad-hoc tools(reporting)
• 7.
  Weka (predictive analytics)
• 8.
  Enterprise Extras Analyzer
• 9.
  Interactive Reporting
• 10.
  Dashboard Designer
• 11.
  Data Integration Scheduler
• 12.
  Support
• 13.
   
• 14.
  OLAP? On-line AnalyticalProcessing Designed for Analytics, not transactions ROLAP (Mondrian) Relational OLAP MOLAP (Palo) Multidimensional OLAP
• 15.
  ROLAP Benefits Datais stored in a Kimball-style star schema Usable by all other tools (reporting, dashboards, etc.) “Cube” is stored in memory Cons Performance while “cube” is being cached
• 16.
  Performance depending onbackend database
• 17.
  MOLAP Benefits Datastored in multidimensional format
• 18.
  Usually highly compressedCons Potentially long processing times to handle permutations
• 19.
  Higher cardinality (dimensionswith millions of records) increases processing
• 20.
  Mondrian Development Life-cycle
• 21.
  ROLAP Optimizations Columnardata stores Built for huge datasets in a conformed dimension format
• 22.
  Highly compresses andscales
• 23.
  Examples: LucidDB,Infobright, InfiniDB
• 24.
  Mondrian Specific OptimizationsAggregate Designer Performs cost/benefit analysis on all permutations of data
• 25.
  Builds SQL queriesthat can be loaded into ETL or plugins (like with LucidDB) and run at set times Cons Have to refresh aggregate data as new data comes in – will get stale otherwise!
• 26.
  Time to refreshis dependent on data set
• 27.
  MDX MultiDimensional Expressions
• 28.
  “SQL” for OLAP
• 29.
  Open standard developedby Microsoft
• 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