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Microsoft Business Intelligence Solution

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  • Spend time building up this slide. Note that the main points on this slide will be covered in the slides that follow.Build 1: Introduces source systems and client access. Mention a common requirement for information workers to analyze and report on this data.Build 2: Should the information workers connect directly to these systems? Remind students of the points on the slide about common information problems: Performance impact, availability, cleanliness, historical context preservation, and end user skills and tools.Build 3: Focuses on source system mirroring. Mention that database mirroring (an availability feature introduced with SQL Server 2008) could make a read-only copy of the database available to reduce the impact on the source database.Build 4: Introduces the data warehouse, which consists of data marts, a multidimensional database, data mining models and data feeds. The data warehouse system can overcome many of the issues raised in Build 2, but it implies that the data must be copied from the source systems…Build 5: Highlights the ETL process. Mention that the data from the source systems needs to be periodically extracted and loaded into the data marts. These data marts commonly have a particular schema design optimized for querying, so the data will need to be transformed. Introduce the term ETL—extract, transform, and load.Build 6: Introduces the staging systems. Performing the ETL in one process may be difficult to achieve because of the complexity of transformations or the need to cleanse the data. Mention that staging systems are optional and that the technologies introduced in this course (e.g., SSIS) may challenge this traditional need. Note that staging is still an important design consideration because it provides convenient restartability of the ETL process without the need to disturb the source systems.Build 7: Manual cleansing may be required to fix problematic data. This is expensive in terms of human resources and time. Mention that the technologies introduced in this course (e.g., SSIS) may be able to address this problem.Build 8: Client access can take many forms—for example, via browsing tools, reports, spreadsheets, dashboards, and so on.. Stress that, ideally, clients extract their data from the “one version of the truth.” Discuss the different types of users: power users, analysts and their different needs.Build 9: Emphasize that this is a continuous process of monitoring, analyzing and planning.
  • Source data can stored in a variety of different data stores and in difference formats.Is usually is not optimized for analytic and reporting needs.A data warehouse can deliver a unified data store that presents cleansed, conformed data for optimized analytics and reporting.
  • Adventure Works Cycles, that manufactures and sells bicycles, bicycle components, clothing,and accessories for North American, European, and Asian markets.When detailed data from the data warehouse is loaded into a multidimensional OLAP database, summarized values are precalculated.
  • Source data can stored in a variety of different data stores and in difference formats.Is usually is not optimized for analytic and reporting needs.A data warehouse can deliver a unified data store that presents cleansed, conformed data for optimized analytics and reporting.
  • In order to populate the data warehouse, periodically data needs to move from the source system(s) to the data warehouse.This is often referred to an ETL process (Extract, Transform and Load).SQL Server Integration Services was designed specifically to perform an ETL processes.
  • Control flow governs the order and precedence of how tasks are executed.
  • Data flows can be developed to extract data from multiple sources, and then integrate and transform that data into a format that is useful for reporting and analytics.
  • Cubes deliver a conceptual model of measures and dimensions.They are best developed on top of data warehouse structures, in particular those designed as star schemas.Cubes deliver rapid ad hoc query responses, and can enrich the model with hierarchies, properties, calculations, KPIs, actions, perspectives and translations.End-users commonly connect directly to cubes and use graphical designers to construct their queries.
  • Numbers without context may be data, but they are not informationWhen data is loaded into a multidimensional OLAP database, metadata is added to the data. Metadata is data about the data. The metadata in an OLAP database includes information about relationships and hierarchies in the data, how the data should be sorted and summarized, and how it should be formatted for presentation. The metadata in the OLAP database is what turns data into information.
  • BI practitioners just call each list an attribute.Because the labels in each list are related to each other and belong in the same attribute, the labels are called members.The Product attribute is the key attribute.
  • The value of Units Sold for each Model is the sum, or aggregation, of the value of Units Sold of the related Products.The Model and Product attribute members are arranged in a hierarchy.
  • Cubes deliver a conceptual model of measures and dimensions.They are best developed on top of data warehouse structures, in particular those designed as star schemas.Cubes deliver rapid ad hoc query responses, and can enrich the model with hierarchies, properties, calculations, KPIs, actions, perspectives and translations.End-users commonly connect directly to cubes and use graphical designers to construct their queries.
  • Each column in a fact table is typically either a key column or a fact column, but it is also possible to have other columns for reference purposes—for example, purchase order numbers or invoice numbers.A fact table contains a column for each measure.
  • Many dimension attributes can be used to create groups of dimension records, and then the related facts can be summarized for each group.For example, Product dimension records can be grouped into Bikes and Accessories categories, and then the number of Units Sold for each category can be calculated.
  • In an operational database, it is critical for data to be consistent across the entire application:If you change a customer’s address in one part of the system, you want the changed addressto be immediately visible in all parts of the system. Because of this need for consistency, operationaldatabases tend to be broken up into many tables so that any value is stored onlyonce in a single table.Any time the value is needed, a join to the table containing the valuecan be created. Ensuring that a value is stored in only one place is one element of a processcalled normalization, and it is very important in operational database systems.If you execute a report using data from the data warehouse, however, many joins can make the query slow. For example, suppose you want to see Sales Amount for the Bikes category for the year 2011. To aggregate by Bikes, you have to join each row in the fact table to the Product table, and then to the Subcategory table, and then to the Category table. To aggregate by the year 2011, you also have to join the fact table to the Month table, to the Quarter table, and finally to the Year table. And you have to do all those joins for all the rows in the fact table, discard the rows that are not related to the Bikes category and the year 2011, and then sum Sales Amount in the remaining rows. Joining all of the Product dimension and Date dimension tables to the fact table makes the query for this report much slower than if all the Product attributes were in a single table and all the Date attributes were in a single table.
  • Cubes deliver a conceptual model of measures and dimensions.They are best developed on top of data warehouse structures, in particular those designed as star schemas.Cubes deliver rapid ad hoc query responses, and can enrich the model with hierarchies, properties, calculations, KPIs, actions, perspectives and translations.End-users commonly connect directly to cubes and use graphical designers to construct their queries.
  • The columns containing numerical data in a fact table correspond to measures in a dimensionalmodel, so each fact table is a group of measures. Analysis Services organizes informationin a logical construct called a measure group that corresponds to a single fact table andits related dimensions.
  • A report of sales by product subcategory by quarter may require several minutes to run, even if you have only 50 subcategories and 20 quarters. But if you pre-summarize the data into an aggregate table that includes only subcategories and quarters, the aggregate table will have at most 1,000 rows (50 subcategories times 20 quarters gives a maximum of 1,000 possible rows), and a report requesting totals by subcategory and by quarter will not take very long to execute.
  • Areport of sales by product subcategory by quarter may require several minutes to run, evenif you have only 50 subcategories and 20 quarters. But if you pre-summarize the data into anaggregate table that includes only subcategories and quarters, the aggregate table will haveat most 1,000 rows (50 subcategories times 20 quarters gives a maximum of 1,000 possiblerows), and a report requesting totals by subcategory and by quarter will not take very longto execute.Conceptually, eachmeasure group contains all the detail values stored in the fact table, but that doesn’t meanthat the measure group must physically copy and store all of that data. If you choose, youcan make the measure group dynamically retrieve values as needed from the fact table. Inthis case, you’re using the measure group only to define metadata. This is called relationalOLAP, or ROLAP. For faster query performance, you can have Analysis Services load the detailvalues into its own proprietary storage structure and precalculate aggregate values. Thiswill provide improved query performance. This is called multidimensional OLAP, or MOLAP.Analysis Services allows you, the cube designer, to decide to use MOLAP or ROLAP. Asidefrom performance differences, where the detail values are physically stored is completelyinvisible to a user of a cube. Whether you use MOLAP or ROLAP, when you execute a querythe results are stored in memory, on a space-available basis, to make subsequent queriesfaster. You can think of MOLAP storage as a disk-based cache that allows the Analysis Serverto load the memory cache much faster than if it had to retrieve data from a relational datawarehouse.
  • Business intelligence

    1. 1. Business IntelligenceMICROSOFT BI SOLUTION
    2. 2. Mountains of DataOrganizations have lots of data◦ ERP, CRM, Portal…Data is not in a form that is useful to decision-makers◦ Not easy to review◦ Not informative nor insightfulBASQUANG@HOTMAIL.COM 2
    3. 3. Traditional solutionMRPSCMCRM FinanceOperations FinanceTransaction LayerReporting LayerSalesProcurementBASQUANG@HOTMAIL.COM 3
    4. 4. Data Consolidation SolutionMRPCRMSCM FinanceTransactionLayerShared DataLayerData WarehouseCustomers Sales Procurement Suppliers Operations FinanceSharedReportingBASQUANG@HOTMAIL.COM 4
    5. 5. Business Intelligence SystemA BI system is the solution for gathering data from multiple sources, transforming that data sothat it is consistent and stored in a single location, and presenting the information to you toanalysis and decision making.BASQUANG@HOTMAIL.COM 5
    6. 6. Business Intelligence ProcessInformationGatheringData SourcesDataProcessingData IntegrationAnalysis &ProductionReport CreationDirecting &PlanningAnalytic GroupsConsumers RequirementsDashboards,Reports,Charts…BASQUANG@HOTMAIL.COM 6
    7. 7. Data SourcesStaging AreaManualCleansingData MartsData WarehouseClientAccessClientAccess1: Clients need access to data2: Clients may access data sources directly3: Data sources can be mirrored/replicated to reduce contention4: The data warehouse manages data for analyzing and reporting5: Data warehouse is periodically populated from data sources6: Staging areas may simplify the data warehouse population7: Manual cleansing may be required to cleanse dirty data8: Clients use various tools to query the data warehouse9: Delivering BI enables a process of continuous business improvementBASQUANG@HOTMAIL.COM 7
    8. 8. SQL Server BI StructureData Source LayerData Transformation LayerData Storage and Retrieval LayerAnalytical LayerPresentation LayerText, MS Excel, MS Access, MS SQL, Oracle,…|External Sources1. Extract the data from the multiple sources2. Modify the data to consistent3. Load the data into Data Storage systemData Warehouse in RDBMSTurn data into information (analysis)Multidimensional OLAP DatabaseReporting and Visualization Tools (Dashboard,KPI, Scorecard,…)BASQUANG@HOTMAIL.COM 8
    9. 9. Microsoft Business IntelligencePlatformData Warehouse, Data Marts,Operational Data(SQL Server 2008 R2/Oracle/DB2, Sybase…)Integrate(SQL Integration Services)Analyze(SQL Analysis Services)Report(SQL Reporting Services)Portal(SharePoint)Scorecards, Analytics, Planning(PerformancePoint Service)Report BuilderSSRSEnd-user Analysis(Excel)OfficeSQLInfrastructurePlatformData DeliveryAnalyticApplicationsBASQUANG@HOTMAIL.COM 9
    10. 10. DEMODATA SOURCE AND DATA WAREHOUSESTRUCTUREBASQUANG@HOTMAIL.COM 10
    11. 11. Data Transformation(ETL)SQL INTEGRATION SERVICESBASQUANG@HOTMAIL.COM 11
    12. 12. SQL Server BI StructureData Source LayerData TransformationLayerData Storage andRetrieval LayerAnalytical LayerPresentation LayerText, MS Excel, MS Access, MS SQL, Oracle,…|External Sources1. Extract the data from the multiple sources2. Modify the data to consistent3. Load the data into Data Storage systemData Warehouse in RDBMSTurn data into information (analysis)Multidimensional OLAP DatabaseReporting and Visualization Tools (Dashboard,KPI, Scorecard,…)BASQUANG@HOTMAIL.COM 12
    13. 13. Data Integration in Real WorldExtract datafrom sourcesCleanse &TransformLoad data intodata warehouseBASQUANG@HOTMAIL.COM 13
    14. 14. SSIS ArchitectureSQL Server IntegrationServices (SSIS) serviceSSIS object modelTwo distinct runtime engines:◦ Control flow◦ Data flowBASQUANG@HOTMAIL.COM 14
    15. 15. SSIS ArchitectureSSIS Designer◦ Graphical tool to create and maintain Integration Services packages.Integration Services Runtime◦ Saves the layout of packages, runs packages, and provides supportfor logging, breakpoints, configuration, connections, andtransactions.Tasks and other executable:◦ The Integration Services run-time executables are the package,containers, tasks, and event handlersBASQUANG@HOTMAIL.COM 15
    16. 16. SSIS ArchitectureData Flow engine (pipeline)◦ In-memory buffersData Flow components◦ Sources,◦ Transformations◦ DestinationsBASQUANG@HOTMAIL.COM 16
    17. 17. SSIS ArchitectureObject Model◦ Allow for creating custom components for use in packagesIntegration Services Service◦ Lets you monitor running Integration Services packages and tomanage the storage of packages.BASQUANG@HOTMAIL.COM 17
    18. 18. DEMOINTEGRATION PACKAGEBASQUANG@HOTMAIL.COM 18
    19. 19. BASQUANG@HOTMAIL.COM 19
    20. 20. Data WarehouseANALYTICAL LAYERDATA STORAGE AND RETRIEVAL LAYERBASQUANG@HOTMAIL.COM 20
    21. 21. SQL Server BI StructureData Source LayerData TransformationLayerData Storage andRetrieval LayerAnalytical LayerPresentation LayerText, MS Excel, MS Access, MS SQL, Oracle,…|External Sources1. Extract the data from the multiple sources2. Modify the data to consistent3. Load the data into Data Storage systemData Warehouse in RDBMSTurn data into information (analysis)Multidimensional OLAP DatabaseReporting and Visualization Tools (Dashboard,KPI, Scorecard,…)BASQUANG@HOTMAIL.COM 21
    22. 22. MULTIDIMENSIONAL DATA ANALYSISBASQUANG@HOTMAIL.COM 22
    23. 23. Measure and MetadataMeasure: A summarizable numerical value◦ Sales Dollars, Shipment Units,...Metadata: Data about data◦ Label, Order by,...Units Sold7070Adventure Works Sales Adventure Works SalesMetadataMeasureBASQUANG@HOTMAIL.COM 23
    24. 24. Unit sold by Product and MonthreportProduct Jan 2011 Feb 2011 Mar 2011 Apr 2011Mountain-500 Black, 40 1 3 1 2Mountain-500 Black, 44 2 1Mountain-500 Black, 48 1 2 1Mountain-500 Silver, 40 1 2 1Mountain-500 Silver, 44 1 1 1Mountain-500 Silver, 48 2Road-750 Black, 44 10 7Road-750 Black, 48 5 9Hitch Rack 1 6 6 3BASQUANG@HOTMAIL.COM 24
    25. 25. Grouping-AggregatingAttribute-MemberGrouping – Aggregating: is the wayhumans deal with too much detail◦ Ex: group Products by model, subcategory,and category groupsAttribute: Product (Key), Model, Color,SizeMember◦ Model: Mountain-500, Road-750…◦ Color: Black, Silver◦ Size: 40, 44, 48Product Model Color SizeMountain-500 Black, 40 Mountain-500 Black 40Mountain-500 Black, 44 Mountain-500 Black 44Mountain-500 Black, 48 Mountain-500 Black 48Mountain-500 Silver, 40 Mountain-500 Silver 40Mountain-500 Silver, 44 Mountain-500 Silver 44Mountain-500 Silver, 48 Mountain-500 Silver 48Road-750 Black, 44 Road-750 Black 44Road-750 Black, 48 Road-750 Black 48Hitch Rack Hitch Rackproduct with model name, color, and size attributesBASQUANG@HOTMAIL.COM 25
    26. 26. Hierarchy: Model  ProductJan 2011 Feb 2011 Mar 2011 Apr 2011Mountain-500 3 8 6 6Mountain-500 Black, 40 1 3 1 2Mountain-500 Black, 44 2 1Mountain-500 Black, 48 1 2 1Mountain-500 Silver, 40 1 2 1Mountain-500 Silver, 44 1 1 1Mountain-500 Silver, 48 2Road-750 15 16Road-750 Black, 44 10 7Road-750 Black, 48 5 9Hitch Rack 1 6 6 3Hitch Rack 1 6 6 3Group Units Sold by Model, Product and MonthBASQUANG@HOTMAIL.COM 26
    27. 27. HierarchyHierarchy is created byarranging related attributesinto levelsHierarchy level: 2, 3,…nHierarchy type:◦ Balance (Date)◦ Unbalance (Organization)BASQUANG@HOTMAIL.COM 27
    28. 28. DimensionsJan 2011 Feb 2011 Mar 2011 Apr 2011Mountain-500 3 8 6 6Road-750 15 16Hitch Rack 1 6 6 3Units Sold by Model and Month• Attribute:– Model (3)– Month (4)• Potential number of values: 12 = 3x4BASQUANG@HOTMAIL.COM 28
    29. 29. DimensionsJan 2011 Feb 2011 Mar 2011 Apr 2011Units $ Units $ Units $ Units $WA Hitch Rack 4 $480 3 $360 2 $240Mountain-500 2 $1.105 6 $3.256 5 $2.775 5 $2.750Road-750 9 $4.860 10 $5.400OR Hitch Rack 2 $240 3 $360 1 $120Mountain-500 1 $120 2 $1.105 1 $540 1 $540Road-750 1 $565 6 $3.240 6 $3.240• Attribute:– State (2), Model (3), Month (4), Measure (2: Units sold, Sales dollars)• Potential number of values: 2x3x4x2 = 48BASQUANG@HOTMAIL.COM 29
    30. 30. DimensionsExamples:◦ State attribute belongs to the Geography dimension◦ Model attribute belongs to the Product dimension◦ Month attribute belongs to the Date dimension◦ Units sold and Sale Dollars belongs to the Measure dimensionBASQUANG@HOTMAIL.COM 30
    31. 31. DimensionsThe independent attributes and hierarchies are the dimensionA dimension may contain more than one attributes◦ Ex: Product dimension contain Color and Size attributeDimension also contain hierarchies◦ Ex: Product by Model hierarchy is composed of attributes contained in the Product dimension, so thehierarchy also belongs in the Product dimensionMeasure dimension are displayed on columnsBASQUANG@HOTMAIL.COM 31
    32. 32. DIMENSIONAL DATA WAREHOUSEDATA STORAGE AND RETRIEVAL LAYERBASQUANG@HOTMAIL.COM 32
    33. 33. SQL Server BI StructureData Source LayerData TransformationLayerData Storage andRetrieval LayerAnalytical LayerPresentation LayerText, MS Excel, MS Access, MS SQL, Oracle,…|External Sources1. Extract the data from the multiple sources2. Modify the data to consistent3. Load the data into Data Storage systemData Warehouse in RDBMSTurn data into information (analysis)Multidimensional OLAP DatabaseReporting and Visualization Tools (Dashboard,KPI, Scorecard,…)BASQUANG@HOTMAIL.COM 33
    34. 34. Dimension Data WarehouseDimension Data Warehouse is the data storage and retrieval layer of BI systemIn dimension data warehouse:◦ Dimension are stored in dimension tables◦ Measure are called facts and are stored in fact tablesBASQUANG@HOTMAIL.COM 34
    35. 35. Fact TableState Product Month UnitsSold SalesDollarsOR Hitch Rack Jan 2011 1 $120.00OR Mountain-500 Silver, 40 Jan 2011 1 $565.00OR Mountain-500 Silver, 48 Jan 2011 1 $552.50WA Mountain-500 Silver, 48 Jan 2011 1 $552.50OR Hitch Rack Feb 2011 2 $240.00WA Hitch Rack Feb 2011 4 $480.00• Fact table:– table that stores the detailed values for measures• Key Column:– State, Product, Month• Fact Column:– UnitsSold, SalesDollarsFactSales tableBASQUANG@HOTMAIL.COM 35
    36. 36. Fact TableThe value in the key columns relate the facts in the fact table row to a row in each dimensiontableFact table may have other type of column for reference purposesFact table might contain one or more measure columnsBASQUANG@HOTMAIL.COM 36
    37. 37. Fact TableThe level of detail stored in a fact table is called granularityThe dimensions that a fact table is related to is called dimensionality of the fact tableFacts that have different granularity of different dimensionality must be stored in separate facttablesBASQUANG@HOTMAIL.COM 37
    38. 38. Fact table: Dimension keyActually a fact table almost alwaysuses an integer, called a dimensionkey, for each dimension memberThere must be a dimension table foreach dimension key in a fact tableState Product Month UnitsSold SalesDollars1 483 201101 1 120.001 591 201101 1 565.001 594 201101 1 552.502 594 201101 1 552.501 483 201102 2 240.002 483 201102 4 480.00FactSales table using Dimension keyBASQUANG@HOTMAIL.COM 38
    39. 39. Dimension TableA dimension table contain one row for each member ofthe key attribute of the dimensionThe key attribute has two column:◦ Integer dimension key (PK)◦ Attribute labelA dimension table may contain other columns for otherattributes of the dimensionProductKey Product596 Mountain-500 Black, 40598 Mountain-500 Black, 44599 Mountain-500 Black, 48591 Mountain-500 Silver, 40593 Mountain-500 Silver, 44594 Mountain-500 Silver, 48604 Road-750 Black, 44605 Road-750 Black, 48483 Hitch RackDimProduct Dimension TableBASQUANG@HOTMAIL.COM 39
    40. 40. Dimension tableProductKey Product SubCategory Category Color Size596 Mountain-500 Black, 40 Mountain Bikes Bikes Black 40598 Mountain-500 Black, 44 Mountain Bikes Bikes Black 44599 Mountain-500 Black, 48 Mountain Bikes Bikes Black 48591 Mountain-500 Silver, 40 Mountain Bikes Bikes Silver 40593 Mountain-500 Silver, 44 Mountain Bikes Bikes Silver 44594 Mountain-500 Silver, 48 Mountain Bikes Bikes Silver 48604 Road-750 Black, 44 Road Bikes Bikes Black 44605 Road-750 Black, 48 Road Bikes Bikes Black 48483 Hitch Rack Bike Racks AccessoriesDimProduct Dimension TableBASQUANG@HOTMAIL.COM 40
    41. 41. Aggregatable and AggregateAggregatable: Attributes that can be used to create groupsNon aggregatable attributes are referred to as member properties◦ Ex: List Price, Telephone Number, Street Address…Aggregate: Summary value in the group of aggregatableExample:◦ Aggregatable: Category, Color…◦ Aggregate: Number of Units Sold for each CategoryBASQUANG@HOTMAIL.COM 41
    42. 42. Table structureOLTP: Normalization to make surethat a value is stored in only oneplace- Consistency- More tables with morerelationshipNormalizing each of the dimensiontables so that each dimension hasseveral tables results in a snowflakeschema,BASQUANG@HOTMAIL.COM 42
    43. 43. Table structureOLAP: Denormalizing data to storing redundant values ina single table- redundant- fast queryCreating a single denormalized table for each dimensionresults in a star schema.BASQUANG@HOTMAIL.COM 43
    44. 44. MULTIDIMENSIONAL OLAPANALYTICAL LAYERBASQUANG@HOTMAIL.COM 44
    45. 45. SQL Server BI StructureData Source LayerData TransformationLayerData Storage andRetrieval LayerAnalytical LayerPresentation LayerText, MS Excel, MS Access, MS SQL, Oracle,…|External Sources1. Extract the data from the multiple sources2. Modify the data to consistent3. Load the data into Data Storage systemData Warehouse in RDBMSTurn data into information (analysis)Multidimensional OLAP DatabaseReporting and Visualization Tools (Dashboard,KPI, Scorecard,…)BASQUANG@HOTMAIL.COM 45
    46. 46. Multidimensional OLAPMultidimensional OLAP database resides between the data storage and retrieval layer and thepresentation layerIt converts the relation data warehouse data into a fully implemented dimensional model forcreating analytical reports and data visualizationsBASQUANG@HOTMAIL.COM 46
    47. 47. Measure Group and CubeMeasure group corresponds to a single fact tableMeasure group may contains data for single level of detail and aggregated data forall higher levels of detailCube: Combination of several related measure groups and a set of dimensionsState Product Date Units Sold Sales AmountAll All All 70 31.305WA All All 46 21.235WA Bikes All 37 20.115WA Road Bikes All 19 10.260BASQUANG@HOTMAIL.COM 47
    48. 48. UNDERSTANDING OLAPBASQUANG@HOTMAIL.COM 48
    49. 49. What is OLAP1985.OLTP1993.OLAP• Benefits– Consistently fast response– Metadata-based queries– Spreadsheet-style formulasOnline TransactionProcessingOnline AnalyticalProcessingBASQUANG@HOTMAIL.COM 49
    50. 50. Consistently Fast ResponseCalculating and storing aggregate values and the results of formulas when a cube is loaded(calculation in advance)Aggregate tables can be created to provide fast query resultsBASQUANG@HOTMAIL.COM 50
    51. 51. Metadata-BasedQueriesSQL is suitable for transactionsystem not for reportingapplicationsQuery language for OLAP datasource◦ Multidimensional expression(MDX)SELECT[Store].[Store Country].[Canada].[Vancouver]ON COLUMNS,[Product].[All Products].[Clothing].[Mittens]ON ROWSFROM [Sales]WHERE ([Measures].[Unit Sales],[Date].[2010].[February])SELECT SUM(Sales.[Unit Sales])FROM (Sales INNER JOIN StoresON Sales.StoreID = Stores.StoreID)INNER JOIN ProductsON Sales.ProductID = Products.ProductIDWHERE Stores.StoreCity = VancouverAND Products.ProductName = MittensAND Sales.SaleDate BETWEEN 01-02-2010 AND28-02-2010SQL QueryMDX QueryBASQUANG@HOTMAIL.COM 51
    52. 52. DemoSQL SERVER ANALYSIS SERVICESBASQUANG@HOTMAIL.COM 52
    53. 53. BASQUANG@HOTMAIL.COM 53
    54. 54. Thank you!BASQUANG@HOTMAIL.COM 54

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