essbase all ppts
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ESSBASE
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->YearProduct Line->Product Family->Product Cat->Product sub CatEssbase->Reporting SOL(Hyperion/3rd Party)
Minute->Day->Week->Month->Qtr->YearProduct Line->Product Family->Product Cat->Product sub CatEssbase->Reporting SOL(Hyperion/3rd Party)
It 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 Characteristic
• 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.
Essbase Components
1.Client tier
2.Middle Tier (App tier)
3.Database tier
Essbase Architecture
Architecture
Essbase TerminologyApplication:- 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.
ParentParent
ChildrenChildren
SiblingsSiblings
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
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
7. Roots and Leaf:: The root is the top member in a branchLeaf members have no children. They are also referred to as level 0 members
7. Roots and Leaf:: The root is the top member in a branchLeaf members have no children. They are also referred to as level 0 members
RootRoot
LeafLeaf
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.
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
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
Outline
Introduction To outline An Outline is the tree structure for a
dimension hierarchies. Database outlines define the structure of a
multidimensional 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
Parent :- 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:- 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.
Ancestors:-Ancestors are members in branches above a member. For example, Margin, Profit, and Measures are ancestors of Sales.
Root:-The root is the top member in a branch. Measures is the root for Profit, Inventory, Ratios, and the children of Profit, Inventory, and Ratios.
Leaf 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:- 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:- 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
Member PropertiesYou can specify a broad variety of settings for each member that define the member’s 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
Member PropertiesYou can specify a broad variety of settings for each member that define the member’s 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
Data Blocks Created for Sparse Members Data Blocks Created for Sparse Members An Ideal Configuration with Combination of Dense and Sparse Dimensions
An Ideal Configuration with Combination of Dense and Sparse Dimensions
D, 5
D, 4
S, 2
S, 6
240
Block Size = 20
Block Count = 12
240
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 TypesAssigning 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 Reporting
Expense Reporting -$
Actual Budget VAR
Sales 100 90 10
Payroll 100 90 10
Actual Budget VAR
Sales 100 90 10
Payroll 100 90 -10
Time Balance
Jan Feb Mar QTR1
Sales 10 10 10 30
Inventory 35 10 10 35
Jan Feb Mar QTR1
Sales 10 10 10 30
Inventory 10 10 15 15
TB Last / TB First / TB Avg / TB None
TB LastTB Last
TB FirstTB First
Skipping
Jan Feb Mar QTR1
Sales 10 10 10 30
Inventory
10 10 #Missing #Missing
Jan Feb Mar QTR1
Sales 10 10 10 30
Inventory
10 10 #Missing 10
Skip Missing or 0 / Skip Missing / Skip 0 / Skip None
Expense Reporting Time Balance Skipping
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
DTS Calculation:
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 Dimensions Analytic Services 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:- A dense dimension is a dimension with
a high probability that one or more cells is occupied in every combination of
dimensions.Ex:-Time ,Accounts etc…..
Storage MechanismEssbase stores the data in terms of blocks and cells.
Block:-Analytic Services creates data blocks for combinations of members in the sparse standard dimensions (providing at least one data value exists for the member combination).
Cell:-It is a part in block. It is the combination of all dense dimension stored member combination ,where the value exactly resides.
Each cell occupies 8 bytes.Each block creates an index(i.e sparse combination).Index holds the address of the block.
Potential No. of data blocks:-That is max no. of data blocks we can have multiplication of no. of members from each sparse dimension).
No.of cells:-Multiplication of no. of stored members of each dense dimension.
Block size:-no.of cells*8 bytesCube size:-No.of blocks*block sizeBlock Density:-(no.of data existed cells/Total
no.of cells)*100%.
Determining the Number of Data Blocks in a Database
Period (Dense)
PeriodJan to Dec
Account (Dense)
AccountAccount1Account2Account3
Version (Sparse)
Version1st DraftFinal
Entity (Sparse)
EntityCorp
Each block contains 36 cells= (3 Accounts * 12 Time Periods)
Scenario (Sparse)
ScenarioBudget
Year (Sparse)
Year2007
Attributes•Attribute:-Attribute dimensions are a special type of dimension and are associated with standard sparse dimensions. Essbase does not store the data for attribute dimensions, Essbase dynamically calculates the data when a user retrieves it.
•These should be placed below the std dimensions.
Index N Page files
Index File:-Index file holds the index entries.This index entries holds the physical address of the block.
Extension of Index file is .indIt is just like ess0000n.indThis n is 1,2,3…….etc
Page File:-It holds the compressed blocks.
Extension of Page file is .pagIt is just like ess0000n.pagThis n is 1,2,3…….etc
Member Storage Properties
You can specify data storage properties for members;data storage properties define where and when consolidations are stored.
In Essbase we are having 6 Storage properties.1. Store2. Dynamic Calc3. Dynamic Calc and Store4. Shared member5. Never share6. Label only
Store:-Store the data value with the member.This is the Default setting.
Dynamic Calc:- Not calculate the data value until a user requests it, and then discard the data value.
Dynamic Calc and Store:- Not calculate the data value until a user requests it, and then store the data value.
Shared member:- The data associated with the member comes from another member with the same name.
Never share:- The data associated with the member is duplicated with the parent and its child if an implied shared relationship exists.
Label only:- Although a label only member has no data associated with it, it can still display a value.
The label only tag groups members and eases navigation and reporting. Typically, label only members are not calculated.
Dimension TypesA dimension type is a property that
Analytic Services provides that adds special functionality to a dimension.
Those are:-1.Time2.Accounts3.Currency4.Country5.Attribute6.None
Label Only
Alias
Attribute
Consolidation
Time:-Defines the time periods for which you report and update data. You can tag only one dimension as time. The time dimension enables DTS(Dynamic Time Series),several accounts dimension functions, such as first and last time balances.
Accounts:- Contains items that you want to measure, such as profit and inventory, and makes Analytic Services built-in accounting functionality available. Only one dimension can be defined as accounts.
Here u will get TB properties ,variance reporting ,Two pass calculation .
None:-Specifies no particular dimension type.
Time Balance PropertiesBy default, a parent in the time dimension is calculated
based on the consolidation and formulas of its children.Some times it should not be like this. So,we are telling
the Essbase to calculate the parent member of Time tagged Dimension in a different manner using TB Properties.
So, we are tagging the TB Properties to the Account tagged dimension.
TB Properties are:-1.None2.TB First3.TB Average4.TB Last
TB None:-No special property is assigned.TB First:-Set the TB First when you want the parent value to
represent the value of the first member in the branch.TB Last:-Set the TB Last when you want the parent value to
represent the value of the last member in the branch.TB Average:-Set the time balance as average when you want the parent
value to represent the average value of its children.
Time Balance
Jan Feb Mar QTR1
Sales 10 10 10 30
Inventory 35 10 10 35
Jan Feb Mar QTR1
Sales 10 10 10 30
Inventory 10 10 15 15
TB Last / TB First / TB Avg / TB None
TB LastTB Last
TB FirstTB First
Skip Properties
If you set the time balance as first, last, or average, set the skip property to tell Analytic Services what to do when it encounters missing values or values of 0.
Skip None:-Does not skip data when calculating the parent value.
Skip Missing:-Skips #MISSING data when calculating the parent value.
Skip Zeros:-Skips data that equals zero when calculating the parent value.
Skip Missing and Zeros:-Skips both #MISSING data and data that equals zero when calculating the parent value.
Skipping
Jan Feb Mar QTR1
Sales 10 10 10 30
Inventory
10 10 #Missing #Missing(TB
Last)Jan Feb Mar QTR1
Sales 10 10 10 30
Inventory
10 10 #Missing 10(TB First)
Skip Missing or 0 / Skip Missing / Skip 0 / Skip None
Variance ReportingVariance reporting properties determine how Analytic
Services calculates the difference between actual and budget data in a member with the @VAR or @VARPER function in its member formula.
For Non-Expense members:- Variance=Actual-Budget;For Expense members:- Variance=Budget-Actual;Essbase does n’t know which member is expense/Non-
expense.So,we are having the Expense and Non-expense
properties. And we need to tag those properties.
Non-Expense Reporting
Expense Reporting -$
Actual Budget VAR
Sales 100 90 10
Payroll 100 90 10
Actual Budget VAR
Sales 100 90 10
Payroll 100 90 -10
Two-Pass calculation
Two-pass, this default label indicates that some member formulas need to be calculated twice to produce the desired value.
The two-pass property works only on members of the dimension tagged as accounts and on members tagged as Dynamic Calc and Dynamic Calc and Store.
2 Pass Calculation:
Jan Feb Mar Qtr1
Sales 1000 1000 1000 3000
Profit 100 100 100 300
Profit%
10 10 10 30Mark Profit% as 2Pass. After the full calculation is over, it comes back and again makes it 10.
Jan Feb Mar Qtr1
Sales 1000 1000 1000 3000
Profit 100 100 100 300
Profit%
10 10 10 10
Currency:-Separates local currency members from the base currency defined in the application. This dimension type is used only in the main database and is only for currency conversion applications. The base currency for analysis may be US dollars, and the local currency members may contain values that are based on the currency type of their region.
Country:-Contains data about where business activities take place. In a country dimension, you can specify the type of currency used in each member.
Attributes
Attribute:-Contains members that can be used to classify members of another, associated dimension.
These define the characteristics of the std dimensions.These are for the additional analysis.By default Dynamic Calc(No Storage).Steps to create Attributes:-1.Create the Attribute dimension.2.Associate the dimension to a specific Sparse dimension.3.Add the attribute Members to individual members of
std Sparse dimension.
Rules to create Attribues
1.One attribute dimension can be associated to only one std Sparse Dimension.
2.One Std Sparse Dimension can have multiple attribute dimensions.
3.Only level 0 members of attribute dimension can be associated to the members of std sparse members.
4. Std Sparse Dimension member can have multiple attribute members from different attribute dimensions.
5.We can associate attribute members to the same level members.
DTS(Dynamic Time Series)In order to calculate period-to-date values
dynamically, you need to use a Dynamic Time Series member for a period on the dimension tagged as time.
Analytic Services provides eight predefined Dynamic Time Series members:
● H-T-D (History-to-date)● Y-T-D (Year-to-date)● S-T-D (Season-to-date)● P-T-D (Period-to-date)● Q-T-D (Quarter-to-date)● M-T-D (Month-to-date)● W-T-D (Week-to-date)● D-T-D (Day-to-date)
These eight members provide up to eight levels of period-to-date reporting. How many members you use and which members you use depends on the data and the database outline.
For example, if the database contains hourly, daily, weekly, monthly, quarterly, and yearly data, you can report day-to date (D-T-D), week-to-date(W-T-D), month-to-date(M-T-D), quarter to-date (Q-T-D), and year-to-date (Y-T-D) information.
DTS(Dynamic Time Series)
For present month FEB, Q-T-D(FEB) 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.
We can use only level 0 members with the DTS members.
User Defined Attributes (UDA)
●Works similar to the Attribute dimensions. ●Can be used across the dimensions and across
the levels.●Can be used on dense or sparse.●Used to group members, these will be helpful
in calc scripts. ● You cannot create a UDA on shared members.● You cannot create a UDA on members of
attribute dimensions.
AliasesAliases are names that can be used in place of the
main member name. Aliases are commonly used for storing descriptions and for providing alternative naming conventions.
The Member name and Alias name should be unique throughout the Outline.
There can be multiple Alias to a member. To implement this, we need to have multiple Alias Tables.
LRO(Linked Reporting Object)
An LRO is an artifact associated with a specific data cell in an Analytic Services database.
LROs provide additional information on a cell.
Users create linked objects through Spreadsheet Add-in by selecting a data cell and choosing a menu item.
There is no limit to the number of objects you can link to a cell.
Types Of LRO’S
1.Cell note:- A text annotation
2.File:- An external file, such as a Microsoft Word document, an Excel spreadsheet, a scanned image, an audio clip, or an HTML file (for example, mypage.htm).
3.URL:- An acronym for Uniform Resource Locator. A string that identifies the location of a resource on the
World Wide Web, such as a document, image, downloadable file, service, electronic mailbox, or other resource.
Life Cycle Of Essbase
1.Creating the Database2.Dimensional Building3.Data Loading4.Performing the Calculations5.Generating the Reports
Dimensional Building
Two Methods to build Dimensions1.Manually2.Dynamically(Using Rules file) a. Generation Reference Method b. Level Reference Method c. Parent-Child Method d. Add as child of the specified parent e. Add as sibling at the lowest level f. Add as sibling to a member with a
matching string
Generation Reference:-For Top-down data. Each record specifies the parent’s name, the child’s
name, the children of that child , and so forth.Level Reference Method:-For Bottom-up data.Each record specifies the name of the member , the
name of its parent, the name of its parent’s parent, and so forth.
Parent-Child Method:- Parent followed by its child. Each record specifies the name of the parent and
the name of the new child member, in that order, although they can specify other information as well.
Loading OutlinesHalf Quarter MonthH1 Qtr1 JanH1 Qtr1 FebH1 Qtr1 MarH1 Qtr2 AprH1 Qtr2 MayH1 Qtr2 JunH2 Qtr3 JulH2 Qtr3 AugH2 Qtr3 SepH2 Qtr4 OctH2 Qtr4 NovH2 Qtr4 Dec
Time.txtH1,Qtr1,JanH1,Qtr1,FebH1,Qtr1,MarH1,Qtr2,AprH1,Qtr2,MayH1,Qtr2,JunH2,Qtr3,JulH2,Qtr3,Aug
Steps for Dimensional Building
1.Create a rules file. 2.Open the data source file 3.Set the file delimiters for the data source 4.Define the fields 5.Set the build method 6.Validate the Rules file 7.Save the Rules file and close the Rules file 8.Load data
Loading Outlines (contd.):
Loading Outlines (contd.):
Loading Outlines (contd.):
Allow Property Change:
Loading Outlines (contd.):
Loading Outlines (contd.):
Defining the Dimension from the DLR:
Loading Outlines (other options in Excel):
Profit + Income + Sales +Profit + Income + COGS +Profit + Expenditure - Marketing +Profit + Expenditure - Payroll +Profit + Expenditure - Misc +Profit + Tax -Assets ~X Inventory ~ Opening InventoryAssets ~X Inventory ~ Colsing Inventory ~Ratios ~X Margin % ~Ratios ~X Closing % ~
Parent/Child Reference:Measures Profit +Profit Income +Income Sales +Income COGS -Profit Expenditure -Expenditure Marketing +Expenditure Payroll +Expenditure Misc +Profit Tax -Measures Assets ~XAssets Inventory ~Inventory Opening Inventory +Inventory Colsing Inventory ~Measures Ratios ~XRatios Margin % ~Ratios Closing % ~
Parent/Child Reference (contd.):
Data Loading
Types of data loading are 3.
1.Free form data loading 2.Using Rules file 3.Excel lock &send
Free form data Loading:-If the source file format is 100% matching with
the outline format ,then only we will go for Free form data loading(No use of Rules file).
Excel lock &send:-Through Excel we will enter the data and
submit the data.
Using Rules file:-1.Create a rules file.2.Open the data source file3.Set the file delimiters for the data source4.Define the fields5.Check the data field for the last field(if it
is having single data field)6.Validate the Rules file7.Save the Rules file and close the Rules file8.Load data
Loading Data (contd.):
Loading Data (contd.):
Loading Data (other file formats):
1. Accounts
2. Time
3. Dense
4. Sparse
5. Two – Pass
Order of Computation:
1
2
3
4
5
6
7
8
9
Within Accounts:
Member Formula Vs Calc Script:
Member Formula
CALC Script
Mention only RHS
Mention LHS and RHS
For a Member For the Whole Database
Stored in the Outline
Stored Externally
Intelligent Calculation:
•After any change in data, all the relevant blocks are marked Dirty.
•On running the Default Calc, with Intelligent Calc ON, it optimizes calculation by only calculating the Dirty blocks.
•It can be turned ON or OFF by the command:
SET UPDATECALC ON/OFF;
Backups
There are two methods of backing up a database:
● File system backup● Data export in a text format
Backing up Files During Run-time
If any Essbase databases must be running at the time of the backup, follow these steps:
1. Placing a Database in Read-Only Mode
2. Performing a File Backup 3. Returning Database to Read-
Write Mode
Placing a Database in Read-Only Mode:-Placing the database in read-only (or
“archive”) mode protects the database from updates during the backup process. After you perform the backup using the third-party backup utility of your choice, return the database to read-write mode.
To place a database in read-only mode, use a tool:
Data export
The amount of data to export:1.All data2. Level 0 blocks only (blocks containing only
level 0 sparse member combinations. Note that these blocks may contain data for upper level dense dimension members.)
3. Data from input blocks only (blocks containing data from a previous data load or spreadsheet Lock & Send)
We can export data in a columnar or non-columnar format.
In each row, the columnar format displays a member name from every dimension. Names can be repeated from row to row.
The columnar format provides a structure to the exported data, so that it can be used for further data processing by applications other than Analytic Services tools; for example,relational databases.
In non-columnar format, sparse members identifying a data block are included only once for the block. Because the export file in non-columnar format is smaller than in columnar format, reloading a file in non-columnar format is faster.
Automation(ESSCMD):-
For Scheduling the loading process. Following steps need to be followed.
1. Open notepad and write the following code as shown below, and save it as .scr extension file. (Ex: Sample5.scr)
2. Open notepad and write a batch file as shown below and save it as .bat extension file. (Ex: Sample5.bat)
3. Go to control panel and click on scheduled tasks and Add scheduled task as shown below Click on next following window appears
4. When u click next the following window appears then click browse
5. select the .bat (Ex: Sample5.bat) as shown below and select when the task need to be performed
6. Select the time an the no of days in the following window
7. Enter the Username and password and click next the following window appears
8. After entering the system user name and password click next following window appears then click finish.
Windows will perform the task for the scheduled day and time.
Windows will perform the task for the scheduled day and time.
Report Scripts
•Select the application and the database, and click the Report Scripts
• Click the New button to open the Report Editor
// This is a simple report script example
// Define the dimensions to list on the current page, as below
<PAGE (Market, Measures)
// Define the dimensions to list across the page, as below
<COLUMN (Year, Scenario)
// Define the dimensions to list down the page, as below
<ROW (Product) // Select the members to include in the report Sales
<ICHILDREN Market Qtr1 Qtr2 Actual Budget Variance
<ICHILDREN Product
// Finish with a bang
• Choose File > Save, and type Myrept1 for the report script object name, and save it on the server (the default).
• Choose Report > Run.
Essbase Partitioning
Partition is the piece of a database that is shared with another database.
Partitions contain the following parts :
• Data source information
• Data target information
• Login and password
• Type of partition
• Shared areas
• Member mapping information
• State of the partition
Types of Partitions1.Replicated Partition2.Transparent Partition3.Linked partition
Replicated Partition:- A replicated partition is a copy of a portion of the data source that is stored in the data target.
Some users can then access the data in the data source while others access it in the data target.
Transparent Partition:-A transparent partition allows users to manipulate data that is stored remotely as if it were part of the local database.
The remote data is retrieved from the data source each time that users at the data target request it. Users do not need to know where the data is stored, because they see it as part of their local database.
Because the data is retrieved directly from the data source, users see the latest version of the data.
When they update the data, their updates are written back to the data source. This process means that other users at both the data source and the data target have immediate access to those updates.
Linked Partition:- A linked partition connects two different databases with a data cell. When the end user clicks the linked cell in the data target, you drill across to a second database, the data source, and view the data there.
Linked Partition