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# Essbase beginner's guide olap fundamental chapter 1

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### Essbase beginner's guide olap fundamental chapter 1

1. 1. Document: Essbase Beginner’s Guide Chapter-I Description: The purpose of this tutorial is to provide you with an understanding of the OLAP concepts. What all you should know to kick start learning Hyperion Essbase. History:Version Description Change Author Publish Date0.1 Initial Draft Gaurav Shrivastava 24-Nov-20100.1 1st Review Amit Sharma 25-Nov-2010 Table of Contents Title Page No. 1). On-Line Analytical Processing……………………………………....4 Essbase Beginner’s Guide ©Business Intelligence Solution Providers 1 Learnhyperion.wordpress.com
2. 2. 2). Cube…………………………………….……………….…..….………..5 3). Slicing………………………………………..…………………………..6 4). Dicing……………………………….……….………..…..…….…..…...9 5). Rotating……………………………..………………………..…………10 6). Dimensions……………………………………………………….…….…..…11 7). Categories……………………………………….…………..………..….…….12 8). Measures……………………………………………………………..…13 9). Nesting…………………………………………………………….....….14 10). Aggregations……………………………………….…….……….….15 11). Multi Dimension……………………………………….…………….16 11). Summary……………………………………..…………….………….16 The purpose of this tutorial is to provide you with an understanding of the OLAP concepts.Upon successful completion of this tutorial, you will be able to: 1. Define OLAP 2. Explain OLAP cubes 3. Describe Dimensions 4. Explain Categories 5. Describe Measures 6. Describe Nesting 7. Explain Aggregation 8. Multidimensional Database Essbase Beginner’s Guide ©Business Intelligence Solution Providers 2 Learnhyperion.wordpress.com
3. 3. On-Line Analytical Processing (OLAP)What is OLAP? OLAP is more than an acronym that means Online Analytical Processing. OLAP is acategory of software tools that provides analysis of data stored in a database. With OLAP, analysts,managers, and executives can gain insight into data through fast, consistent, interactive access to awide variety of possible views. Stated another way, OLAP is a category of applications andtechnologies for collecting, managing, processing, and presenting multidimensional data foranalysis and management purposes. A widely adopted definition for OLAP used today in five keywords is: Fast Analysis of Shared Multidimensional Information (FASMI).Fast refers to the speed that an OLAP system is able to deliver most responses to the end user.  Analysis refers to the ability of an OLAP system to manage any business logic and statistical analysis relevant for the application and user. In addition, the system must allow users to define new ad hoc calculations as part of the analysis and report without having to program them.  Shared refers to the ability of an OLAP system being able to implement all security requirements necessary for confidentiality and the concurrent update locking at an appropriate level when multiple write access is required.  Multidimensional refers to a concept that is the primary requirement to OLAP. An OLAP system must provide a multidimensional view of data. This includes supporting hierarchies and multiple hierarchies.  Information refers to all of the data and derived data needed, wherever the data resides and however much of the data is relevant for the application. Cubes  What is an OLAP Cube? As you saw in the definition of OLAP, the key requirement is multidimensional. OLAP achieves the multidimensional functionality by using a structure called a cube. The OLAP cube provides the multidimensional way to look at the data. The cube is comparable to a table in a relational database.  The specific design of an OLAP cube ensures report optimization. The design of many databases is for online transaction processing and efficiency in data storage, whereas OLAP cube design is for efficiency in data retrieval. In other words, the storage of OLAP cube data is in such a way as to make easy and efficient reporting. A traditional relational database treats all the data in a similar manner. However, OLAP cubes have categories of data called dimensions and measures. For now, a simple definition of dimensions and measures will suffice. A measure represents some fact (or number) such as cost or units of service. A dimension represents descriptive categories of data such as time or location.  The term cube comes from the geometric object and implies three dimensions, but in actual use, the cube may have more than three dimensions.  The following illustration graphically represents the concept of an OLAP cube. Essbase Beginner’s Guide ©Business Intelligence Solution Providers 3 Learnhyperion.wordpress.com
4. 4. Date 1Qtr 2Qtr 3Qtr 4Qtr sum ct TVProdu PC U.S.A VCR sum Canada Country Mexico sum Slicing A slice is a subset of a multidimensional array corresponding to a single value for one or more members of the dimensions not in the subset. For example, if the member Actual is selected from the Scenario dimension, then the sub-cube of all the remaining dimensions is the slice that is specified. The data omitted from this slice would be any data associated with the non- selected members of the Scenario dimension, for example Budget, Variance, Forecast, etc. From an end user perspective, the term slice most often refers to a two- dimensional page selected from the cube. Essbase Beginner’s Guide ©Business Intelligence Solution Providers 4 Learnhyperion.wordpress.com
5. 5. Figure 2: Slicing-Wireless MouseEssbase Beginner’s Guide ©Business Intelligence Solution Providers 5 Learnhyperion.wordpress.com
6. 6.  Figure 2 illustrates slicing the product Wireless Mouse. When you slice as in the example, you have data for the Wireless Mouse for the years and locations as a result. Stated another way, you have effectively filtered the data to display the measures associated with the Wireless Mouse product. Figure 3: Slicing-Asia Figure 3 illustrates slicing the location Asia. When you slice as in the example, you have data for Asia for the product and years as a result. Stated another way, you have effectively filtered the data to display the measures associated with the Asia location. Essbase Beginner’s Guide ©Business Intelligence Solution Providers 6 Learnhyperion.wordpress.com
7. 7. Figure 1: OLAP Cube  In figure 1, time, product, and location represent the dimensions of the cube, while 174 represents the measure. Recall that a dimension is a category of data and a measure is a fact or value.  Three important concepts associated with analyzing data using OLAP cubes and an OLAP reporting tool are slicing, dicing, and rotating.Dicing  A related operation to slicing is dicing. In the case of dicing, you define a sub-cube of the original space. The data you see is that of one cell from the cube. Dicing provides you the smallest available slice.  Figure 4 provides a graphical representation of dicing. Essbase Beginner’s Guide ©Business Intelligence Solution Providers 7 Learnhyperion.wordpress.com
8. 8. Figure 4: DicingRotatingRotating changes the dimensional orientation of the report from the cube data. For example,rotating may consist of swapping the rows and columns, or moving one of the row dimensions intothe column dimension, or swapping an off-spreadsheet dimension with one of the dimensions inthe page display (either to become one of the new rows or columns), etc. You also may hear theterm pivoting. Rotating and pivoting are the same thing.DimensionsRecall, that a dimension represents descriptive categories of data such as time or location. In otherwords, dimensions are broad groupings of descriptive data about a major aspect of a business,such as dates, markets, or products. The value of OLAP in reporting data is having levels within thedimensions. Each dimension includes different levels of categories. Dimension levels allow you toview general things about your data and then look at the details of your data.Think of the levels of categories as a hierarchy. For example, your OLAP cube could have a timedimension. The time dimension then could have year, quarter, and month as the levels, as inFigure 6. Another example is location as a dimension. For the location dimension, you could haveregion, country, and city as the levels (shown in Figure 6). An important concept to OLAP isdrilling. Drilling refers to the ability to drill-up or drill-down. These levels of categories(hierarchies) are what provide the ability to drill-up or drill-down on data in an OLAP cube. When Essbase Beginner’s Guide ©Business Intelligence Solution Providers 8 Learnhyperion.wordpress.com
9. 9. you drill-down on a dimension, you increase the detail level of viewing the data. For example, youstart with the year and view that data, but you want to see the data by each quarter of the year.You would drill-down to see the quarterly data. You could drill-down again to see the monthlydata within a specific quarter.CategoriesDimensions have members or categories. A category is an item that matches a specific descriptionor classification such as years in a time dimension. Categories can be at different levels ofinformation within a dimension. You can group any category into a more general category. Forinstance, you can group a set of dates into a month, a set of months into quarters, and a set ofquarters into years. In this example, years, quarters, and months are all categories of the timedimension.Categories have parents and children. A parent category is the next higher level of anothercategory in a drill-up path. For example, 2003 is the parent category of 2003 Q1, 2003 Q2, 2003Q3, and 2003 Q4. A child category is the next lower level category in a drill-down path. Forexample, January is a child category of 2003 Q1.Figure 7 below shows you the time, product, and location dimensions with the parent and childrencategories. In the time dimension, you see the parent Year Category and the children QuarterCategory and Month Category. Essbase Beginner’s Guide ©Business Intelligence Solution Providers 9 Learnhyperion.wordpress.com
10. 10. Figure 7: CategoriesMeasuresThe tutorial to this point has provided you with a definition of OLAP, a description of cubes, themeaning of dimensions, and a description of categories. The focus now is an explanation ofmeasures.The measures are the actual data values that occupy the cells as defined by the dimensionsselected. Measures include facts or variables typically stored as numerical fields, which providethe focal point of investigation using OLAP. For instance, you are a manufacturer of cellularphones. The question you want answered is how many xyz model cell phones (productdimension) a particular plant (location dimension) produced during the month of January 2003(time dimension). Using OLAP, you found that plant a produced 2,500 xyz model cell phonesduring January 2003. The measure in this example is the 2,500.Additionally, measures occupy a confusing area of OLAP theory. Some believe that measures arelike any other dimension. For example, one can think of a spreadsheet containing cell phonesproduced by month and plant as a two-dimensional picture, but the values (measures)—the cellphones produced—effectively form a third dimension. However, although this dimension doeshave members (e.g. actual production, forecasted production, planned production), it does nothave its own hierarchy. It adopts the hierarchy of the dimension it is measuring, so production bymonth consolidates into production by quarter, production by quarter consolidates intoproduction by year.The example in Figure 8 shows the measure Volume of Product. Each value in Figure 8 is themeasure of Volume of Product per year listed by product. From Figure 8, ABC Company produced84,000 modems in 2003. Essbase Beginner’s Guide ©Business Intelligence Solution Providers 10 Learnhyperion.wordpress.com
11. 11. Figure 8: MeasuresNestingNesting is a function in which you have more than one dimension or category in a row or column.Nesting provides a display that shows the results of a multi-dimensional query that returns a sub-cube, i.e., more than a two-dimensional slice or page. The column/row labels will display the extradimensionality of the output by nesting the labels describing the members of each dimension.Figure 9 illustrates nesting. In the top table, the nested dimensions are time (year) and location(countries) in rows. By nesting in this example, you can see the volume of products for each NorthAmerican Country in 2003. In the bottom table, the nested dimensions are products (devices) andlocation (countries) in columns. By nesting in this example, you can see the volume of products byquarter for each North American Country for 2003 Q1 and Q2. Figure 9: NestingAggregationOne of the keywords for OLAP is fast. Recall that fast refers to the speed that an OLAP system isable to deliver most responses to the end user. Essential for OLAP is to produce fast query times.This is one of the basic tenets for OLAP—the capability to naturally control data requires quickretrieval of information. In general, the more calculations that OLAP needs to perform in order toproduce a piece of information, the slower the response time. Consequently, to keep query timesfast, pieces of information that users frequently will access, but need to be calculated, are pre-aggregated. Pre-aggregated data means that the OLAP system calculates the values and thenstores them in the database as new data. An example of a type of data that may be pre-calculated issummary data, such as failure rate for days, months, quarters, or years. Essbase Beginner’s Guide ©Business Intelligence Solution Providers 11 Learnhyperion.wordpress.com