essbase all ppts
Post on 16-Nov-2014
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What is Essbase?
It is a multidimensional database that enables Business Users to analyze Business data in multiple views/prospective and at different consolidation levels. It stores the data in a multi dimensional array.
Minute->Day->Week->Month->Qtr->Year Product Line->Product Family->Product Cat->Product sub Cat Essbase->Reporting SOL(Hyperion/3rd Party)
Essbase CharacteristicIt has the following characteristics. Works with multidimensional data and rollup hierarchies in dimensions. Gets its information from other systems. Deal with some level of summarised data not transaction Can be adapted to many different reporting and analysis environment. Here we are having 200 Calc functions for complex calculations. Without using ETL also we can do the data load using Rules file (DIM, Essbase Integration Services).
Essbase OLAP Server A MDDB for storing data for unlimited number of dimensions ie. Time, Accounts, Product etc. It manages analytical data models, data storage, calculation and data security. [Extended spreadsheet Database] Spreadsheet Add-in (Client) This enables analysis of the data stored in the Essbase server. Essbase Application Tools It is used for extending Essbase applications. It includes currency conversion, SQL Interface, Spreadsheet Toolkit and APIs. Essbase Partitioning This makes it easy to design and administer databases that spans Essbase application or servers. You can cope a slice of large database to work with locally or you can link from your database to other databases.
tier Tier (App tier) tier
Essbase Terminology Application:-
It is the combination of databases and the related files which to cater a specific requirement. Database:-It is a MDDB which stores the data in terms of cubes. Outline/Cube:- It is the structure of the database ,where we can add the unlimited number of dimensions, members ,Consolidation operators, formulas, aliases, storage properties etc. Dimension:-It is the view/prospective of the business data where the business users can analyze the business data. The dimension represents the highest consolidation level in the database outline. Member:-Members are the individual components of a dimension.
i) Outline: Actual Cube. ii) DLR: Data Load Rules. Loads Data as well as outline definition. iii) Calc Scripts: User defined calculations. iv) Reporting: Reporting tools/formats for end users.
Terminology (contd.):5. Member. Subset of the dimension / values of the dimension. 6. Cell Reference. Contains one and only one member from each and every dimension in the outline. 7. Roots and Leaf:: The root is the top member in a branch Leaf members have no children. They are also referred to as level 0 members 8. Parent, Children, Siblings. A parent is a member that has a branch below . A child is a member that has a parent above it. Siblings are child members of the same immediate parent Root Parent Children
8. Generation. Generation starts from G(1) at Dimension. Each children of a G(i) member will be G(i+1). Any member can have only one Generation. 9. Level. Level starts from L(0) at a member without any children. The parent of a L(i) member will be L(i+1). Any member can have more than one Levels.
10. Ancestors, Descendents, Descendants are members in branches below a parent. e.g Profit, Inventory, and Ratios are descendants of Measures. The children of Profit, Inventory, and Ratios are also descendants of Measures. Ancestors are members in branches above a member. e.g Margin, Profit, and Measures are ancestors of Sales
An Outline is the tree structure for a dimension hierarchies. Database outlines define the structure of a multidimensional
Introduction To outline
database, including all the dimensions, members, aliases, properties, types, consolidations, and mathematical relationships. The structure defined in the outline determines how data is stored in the database. When a database is created, Analytic Services creates an outline for that database automatically. The outline has the same name as the database (dbname.otl). For example, when the Basic database is created within the Sample application, an outline is created in the following directory: ARBORPATH/app/sample/basic/basic.otl
Dimension N Member
In above figure we are having 5 members for Year(i.e Dimension). Those are 1.Jan 2.Feb 3.Mar 4.Qtr1 5.Year
:- A parent is a member that has a branch below it. For example, Margin is a parent member for Sales and Cost of Goods Sold. Child:- A child is a member that has a parent above it. For example , Sales and Cost of Goods Sold are children of the parent Margin. Siblings:-Siblings are child members of the same immediate parent, at the same generation. For example, Sales and Cost of Goods Sold are siblings (they both have the parent Margin). But Marketing (at the same branch level) is not a sibling because its parent is Total Expenses.
Descendants are members in branches below a parent. For example, Profit, Inventory, and Ratios are descendants of Measures. The children of Profit, Inventory, and Ratios are also descendants of Measures. are members in branches above a member. For example, Margin, Profit, and Measures are ancestors of Sales. root is the top member in a branch. Measures is the root for Profit, Inventory, Ratios, and the children of Profit, Inventory, and Ratios.
Node:- Leaf members have no children. They are also referred to as level 0 members. For example, Opening Inventory, Additions, and Ending Inventory are leaf members.
Generation refers to a consolidation level within a dimension. A root branch of the tree is generation 1. Generation numbers increase as you count from the root toward the leaf member.
Level also refers to a branch within a dimension; levels reverse the numerical ordering used for generations. Levels count up from the leaf member toward the root. The root level number varies depending on the depth of the branch.
Member PropertiesMember Properties You can specify a broad variety of settings for each member that define the members storage characteristics and other rollup and reporting behaviors. You can define the following important properties for members: * Aliases * Consolidation operators * Data storage * User-defined attributes (UDAs) * Attribute dimensions
Dense and Sparse
An Ideal Configuration with Combination of Dense and Sparse Dimensions
Data Blocks Created for Sparse Members
Block Size = 20D, 5
Block Count = 12D, 4 240 S, 2 S, 6
Block for P1->N1
Block for P1->N2
We have 12 such blocks of size 20 each. Subsequent blocks will be for: (P1, N1) (P1, N2) (P1, S1) (P1, S2) (P1, N) (P1, S) (P2, N1) (P2, N2) (P2, S1) (P2, S2) (P2, N) (P2, S)
Assigning Dimension Types
Time Dimension: There can only be at most 1 Time Dimension in a Cube. Features are Dynamic Time Series like: Q-T-D, Y-T-D etc. For present month FEB, Q-T-D will give us JAN+FEB. The Names Q-T-D (Quarter To Date) etc has no significance. Whatever functionality, we attach to it, it will function accordingly.
Expense ReportingActual Sales Payroll 100 100 Actual Sales Payroll 100 100 Budget 90 90 Budget 90 90 VAR 10 10 VAR 10 -10
Expense Reporting -$
Time BalanceJan Sales Inventory 10 35 Jan Sales Inventory 10 10 Feb 10 10 Feb 10 10 Mar 10 10 Mar 10 15 QTR1 30 35TB First
QTR1 30 15TB Last
TB Last / TB First / TB Avg / TB None
Skippin gJan Sales Inventory 10 10 Jan 10 10 Feb 10 10 Feb 10 10 Mar 10 QTR1 30
#Missing #Missing Mar 10 #Missing QTR1 30 10
Skip Missing or 0 / Skip Missing / Skip 0 / Skip None
Currency Conversion Properties
Currency conversion properties define categories of currency exchange rates These properties are used only in currency databases on members of accounts dimensions
QTD = G3 Calculate From present month. Calculate in upwards direction. Add only L0. Calculate till you reach G3.
When siblings have different operators, Analytic Services calculates the data in top-down order. Parent1 Member1 (+) 10 Member2 (+) 20 Member3 (-) 25 Member4 (*) 40 Member5 (%) 50 Member6 (/) 60 Member7 (~) 70
(((Member1 + Member2) + (-1)Member3) * Member4) = X (((10 + 20) + (-25)) * 40) = 200 If the result of this calculation is X, Member5 consolidates as follows: (X/Member5) * 100 = Y (200/50) * 100 = 400 If the result of the Member1 through Member4 calculation is Y, Member6 consolidates as follows: Y/Member6 = Z 400/60 = 66.67 Because Member7 is set to No Consolidation(~), Analytic Services ignores Member7 in the consolidation.
Types of DimensionsServices has two types of dimensions. 1.standard dimensions 2. attribute dimensions. Most data sets of multidimensional databases have two characteristics: Data is not smoothly and uniformly distributed. Data does not exist for the majority of member combinations. For example, all products may not be sold in all areas of the country. Analytic
Services maximizes performance by dividing the standard dimensions of an application into two types: dense dimensions and sparse dimensions.
Sparse:- A sparse dimension is a dimension with a low percentage of available data positions filled. Ex:-Product , Market etc. Dense:-