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Mondrian and OLAP Overview
 

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.

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 Mondrian and OLAP Overview Presentation Transcript

    • Mondrian and OLAP Frontends RTP Pentaho User Group Q1 2012 Meetup
    • Pentaho Overview
      • BI Server (Frontend for tools)
      • Report Designer (canned report designer)
      • Mondrian (Schema workbench, aggregate designer)
      • Data Integrator
      • Other ad-hoc tools (reporting)
      • Weka (predictive analytics)
    • Enterprise Extras
      • Analyzer
      • Interactive Reporting
      • Dashboard Designer
      • Data Integration Scheduler
      • Support
    •  
    • OLAP?
      • On-line Analytical Processing
        • Designed for Analytics, not transactions
      • ROLAP (Mondrian)
        • Relational OLAP
      • MOLAP (Palo)
        • Multidimensional OLAP
    • ROLAP
      • Benefits
        • Data is 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
        • Performance depending on backend database
    • MOLAP
      • Benefits
        • Data stored in multidimensional format
        • Usually highly compressed
      • Cons
        • Potentially long processing times to handle permutations
        • Higher cardinality (dimensions with millions of records) increases processing
    • Mondrian Development Life-cycle
    • ROLAP Optimizations
      • Columnar data stores
        • Built for huge datasets in a conformed dimension format
        • Highly compresses and scales
        • Examples: LucidDB, Infobright, InfiniDB
    • Mondrian Specific Optimizations
      • Aggregate Designer
        • Performs cost/benefit analysis on all permutations of data
        • Builds SQL queries that 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!
        • Time to refresh is dependent on data set
    • MDX
      • MultiDimensional Expressions
      • “SQL” for OLAP
      • Open standard developed by Microsoft
    • 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