Introduction to OLAP and Analysis Services from Microsoft

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1 Introduction to OLAP and Analysis Services from Microsoft Josef Schiefer IBM Watson Research Center

2 What is OLAP? Online Analytical Processing - coined by EF Codd in 1994 paper contracted by Arbor Software* Generally synonymous with earlier terms such as Decisions Support, Business Intelligence, Executive Information System OLAP = Multidimensional Database MOLAP: Multidimensional OLAP (Arbor Essbase, Oracle Express) ROLAP: Relational OLAP (Informix MetaCube, Microstrategy DSS Agent) Slide 2

3 OLAP is FASMI Fast Analysis Shared Multidimensional Information Nigel Pendse, Richard Creath - The OLAP Report Slide 3

4 Cubes Slide 4 A cube stores information in a multidimensional structure and is the central object in a multidimensional database. Each cube contains a set of dimensions and measures. Dimensions are derived from the tables and columns in your data that provide the categories you want to analyze. Measures are the quantitative data derived from your data columns

5 Dimensions The dimensions you build should be distinct categories you want to add to cubes in your OLAP database. example: geography, time, or employee dimensions represented in the picture Slide 5

6 Cube and Dimensions Product Dimensions: Product, Region, Time Slide 6 W S N Juice Cola Milk Cream Gel Soap Month Region Hierarchical summarization paths Product Region Time Industry Country Year Category Region Quarter Product City Month Week Office Day

7 Dimensions and Hierarchy Dimensions are the categories used to organize or describe analysis information Dimensions are used to navigate the information and to summarize the details into more aggregate data. Frequently used dimensions include time periods, geography, products, organization, and so on. Often dimensions are hierarchical (World - Continents - Countries) Slide 7

8 Measures = numercial Values Measures are the quantitative data in an OLAP database. For example, values such as sales, budget, cost, and so on, are all examples of measures. Measure values are organized in data cubes according to dimensions Slide 8

9 Aggregations Aggregations greatly improve query efficiency and response time. A cube can hold a number of aggregations. The aggregation amount is based on several factors - the size of the data, the amount of storage space you allocate for aggregation storage, the mode of storage you select, and how much you want to optimize the aggregations design. Slide 9

10 Primary OLAP Problems Rigid, inflexible architectures MOLAP or ROLAP Significant scalability problems Data explosion and sparsity Poor distributed client/server implementation Separation of data warehousing from OLAP tools Lack of integration between user tools and OLAP Difficult to prototype, develop, deploy Time and expense Slide 10

11 MS-AS: Architecture Microsoft Analysis Services are optimized for all OLAP architectures and offers seamless integration MOLAP: aggregations & details managed in an efficient multidimensional store ROLAP: aggregations created in relational store HOLAP: different things to different vendors Aggregations: details in relational, aggregations in MOLAP store Partitions: single logical cube physically divided into multiple MOLAP and ROLAP partitions Virtual cubes: view-like join of multiple MOLAP and ROLAP cubes Slide 11

12 MS-AS: Scalability MS-AS offer major innovation Data explosion managed by partial preaggregation Automatic elimination of sparse storage Partitioned cubes parallel query processing across clustered servers fine tuning of aggregations, to better manage performance and disk space trade-offs Slide 12

13 MS-AS: Scalability Cooperative client/server query management and caching network traffic minimized server queries processed efficiently Microsoft Data Cube Service desktop component ships with next release of Office used with Excel, Access, and Web supports local, offline usage Slide 13

14 Microsoft Data Cube Service Basic architecture: Cache query results and metadata, not disk pages. Algorithms deduce missing data and transform queries Aggregation Filtering Combination Instant reply to cached queries Slide 14

15 MS Data Cube Benefits Efficient distribution of query and calculation processing across client & server Single component spans Microsoft desktop and server platforms & products Unifies the MD data access story across Excel, MS-AS, and SQL Server Enables Microsoft to establish industry standard for MD data access Basis for MS-AS and Excel mobile story Slide 15

16 MS-AS: Integration The Microsoft Analysis Services integrate the maintenance of OLAP with the underlying data warehouse Design the DW structure Create the DW tables/cubes Populate the DW tables/cubes Maintain by incremental loads Optimize by actual usage patterns Manage users, scripts, usage, metadata Multiple data sources (not just SQLS) Slide 16

17 MS-AS: Integration OLE DB for OLAP & ADO MD based upon existing data access technology establishes industry standard for MD data access OLE DB/ODBC enable MS-AS to access data in all major RDBMs Third party client applications Slide 17

18 OLAP Problem: Complexity OLAP products are traditionally difficult to configure, develop, and deploy Arcane tools Heavy consulting Poor integration Slide 18

19 MS-AS: Complexity Intuitive User Interface Wizards, intuitive dialogs, and graphics simplify complex tasks without code Integrated into Microsoft Management Console MS-AS s TCO will be very low cost of NT Server platform leveraged investment in relational technology wide availability of client applications Slide 19

20 3 Tier Architecture & Components Client Tier Excel ActiveX Controls Third Party Applications ADO MD OLE DB for OLAP DCube Data selection & navigation Presentation and charting What-if formulas Client side caching Desktop object model Offline usage Data Warehouse Tier MS-AS Server SQL Server DTS OLTP Source Tier RDBMs Slide 20 MOLAP MS-AS Server HOLAP OLE DB ROLAP Multidimensional calcs MOLAP/ROLAP/HOLAP data Modeling/aggregations Security Metadata management Server side caching Administrative tools Server object model Query distribution

21 Let s go to the demonstration... Slide 21

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