1.
Raimonds SimanovskisMultidimensionalData Analysiswith JRuby

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Raimonds Simanovskis github.com/rsim @rsim .com

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Relationaldata model

4.
SQL is good for detailed data queries Get all sales transactions in USA, CaliforniaSELECT customers.fullname, products.product_name, sales.sales_date, sales.unit_sales, sales.store_salesFROM sales LEFT JOIN products ON sales.product_id = products.id LEFT JOIN customers ON sales.customer_id = customers.idWHERE customers.country = USA AND customers.state_province = CA

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SQL becomes complex for analytical queries Get total sales in USA, California in Q1, 2011 by main product groupsSELECT product_class.product_family, SUM(sales.unit_sales) unit_sales_sum, SUM(sales.store_sales) store_sales_sum FROM sales LEFT JOIN product ON sales.product_id = product.product_id LEFT JOIN product_class ON product.product_class_id = product_class.product_class_id LEFT JOIN time_by_day ON sales.time_id = time_by_day.time_id LEFT JOIN customer ON sales.customer_id = customer.customer_id WHERE time_by_day.the_year = 2011 AND time_by_day.quarter = Q1 AND customer.country = USA AND customer.state_province = CA GROUP BY product_class.product_family

6.
If SQL is not good then we need NoSQL!

7.
Maybe write distributedmap reduce function? http://browsertoolkit.com/fault-tolerance.png

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Multidimensional Data ModelMultidimensional cubes DimensionsHierarchies and levels Measures

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OLAP technologies On-Line Analytical Processing

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Commercial Vendors Oracle Essbase SAP BUSINESSOBJECTSOracle OLAP Cognos Analysis Services

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MDX query language Get total units sold and sales amount in USA, California in Q1, 2011 by main product groupsSELECT {[Measures].[Unit Sales], [Measures].[Store Sales]} ON COLUMNS, [Product].children ON ROWSFROM [Sales]WHERE ( [Time].[2011].[Q1], [Customers].[USA].[CA] )

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http://github.com/rsim/mondrian-olap

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(R)OLAP schemaDimensional model: cubes dimensions (hierarchies & levels) measures, calculated measures MappingRelational model: fact tables, dimension tables joined by foreign keys

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OLAP schema definitionschema = Mondrian::OLAP::Schema.define do cube Sales do table sales dimension Gender, :foreign_key => customer_id do hierarchy :has_all => true, :primary_key => customer_id do table customer level Gender, :column => gender, :unique_members => true end end dimension Time, :foreign_key => time_id do hierarchy :has_all => false, :primary_key => time_id do table time_by_day level Year, :column => the_year, :type => Numeric, :unique_members => true level Quarter, :column => quarter, :unique_members => false level Month,:column => month_of_year,:type => Numeric,:unique_members => false end end measure Unit Sales, :column => unit_sales, :aggregator => sum measure Store Sales, :column => store_sales, :aggregator => sum endend

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Query Builder in Ruby Get total units sold and sales amount in USA, California in Q1, 2011 by main product groupsolap.from(Sales).columns([Measures].[Unit Sales], [Measures].[Store Sales]).rows([Product].children).where([Time].[2011].[Q1], [Customers].[USA].[CA]).execute

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Also more complex queries Get sales amount and profit % of top 50 products sold in USA and Canada during Q1, 2011olap.from(Sales).with_member([Measures].[ProfitPct]). as((Measures.[Store Sales] - Measures.[Store Cost]) / Measures.[Store Sales], :format_string => Percent).columns([Measures].[Store Sales], [Measures].[ProfitPct]).rows([Product].children).crossjoin([Customers].[Canada], [Customers].[USA]). top_count(50, [Measures].[Store Sales])where([Time].[2011].[Q1]).execute

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Demo

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Used in eazybi.com