What Is Siebel
Analytics?
It is a Reporting Tool which provides insight, processing and
pre -built solutions that allow users to seamlessly access critical business
information and acquire the business intelligence required to achieve optimal
results.
Purpose of Siebel Analytics
• To provide data and tools to users to answer questions that are important for
business
• To cater to large & changing data volumes
• To take care of differing requirements
• To replace existing tools that are not aligned to business needs of an
organization
• To leverage and extend common industry practices — Data Warehousing &
Dimensional Modeling
• Other reporting tools are often difficult to master and also static or fixed
and do not allow for interactivity
Siebel Analytics Components
• Intelligence Dashboards
• Siebel Answers
• Siebel Delivers
• Siebel Analytics Server and Siebel Analytics Web
• Siebel Relationship Management Warehouse
• Siebel Analytics Administration Tool
Intelligence Dashboards
A page in an Analytics application that is used to display the results
(corporate and external information) of Siebel Analytics requests and other
kinds of content. Based on your permissions, you can view pre-configured
dashboards, and create or modify dashboards
Siebel Answers
Siebel Answers provides answers to business questions. Allows exploring and
interacting with information, and presenting and visualizing information using
charts, pivot tables, and reports
Results can be saved, organized, and shared in the Siebel Analytics Web Catalog
and can be enhanced through charting, result layout, calculation, and drilldown
features
Siebel Delivers
Interface used to create alerts based on analytics results. Detect specific
results and immediately notify the appropriate person or group through Web,
wireless, mobile, and voice communications channels.
Siebel Analytics Server and Siebel Analytics Web
Is the core server behind Siebel Analytics Provides power behind Siebel
Intelligence Dashboards for access and analysis of structured data distributed
across an organization.
Single request to query multiple data sources, providing information access to
members of the enterprise and, in Web-based applications, to suppliers,
customers, prospects, or any authorized user with Web access
Siebel Relationship Management Warehouse
Is a predefined data source to support analysis of Siebel application data
Is in star schema format
Is included in with Siebel Analytics Applications (not available with
standalone Analytics)
Siebel Analytics Administration Tool
To create and edit repositories and manage Jobs, Sessions, Cache, Clusters,
Security, Joins, Variables, Projects — by Administrator
Is a graphical representation of the three parts (Physical layer, Business
Model and Mapping layer, Presentation layer) of a repository.
Siebel Analytics Architecture : Comprised of five components:
• Clients
• Siebel Analytics Web Server
• Siebel Analytics Server
• Siebel Analytics Scheduler
• Data Sources
Siebel Analytics Web Server
• Provides the processing to visualize the information for client consumption
• Receives data from Siebel Analytics Server and provides it to the client that
requested it
• Uses the web catalog file (.web cat) to store aspects of the application.
Siebel Analytics Web Catalog (web cat)
• Stores the application dashboards, request definitions, pages and filters
• Contains information regarding permissions and accessibility of the
dashboards by groups and users
• Is created when the web server starts
• Is specified in the registry of the machine running the web server
• Is administered using Siebel Analytics Catalog Manager
Siebel Analytics Server
Provides efficient processing to intelligently access the physical data sources
and structures the information
Uses metadata to direct processing
Generates dynamic SQL to query data in the data sources
Connects natively or via ODBC to the RDBMS
Structures results to satisfy requests — Merge results & calculate measures
• Provides the data to the Siebel Analytics Web Server
• Repository file (.rpd)
• Cache
• NQSConfig.ini
• DBFeatures.ini
• Log files
Repository File (rpd)
• Contains metadata that represents the analytical model
• Is created using the Siebel Analytics Administration Tool
Cache
• Contains results of queries
• Is used to eliminate redundant queries to database and Speeds up results
processing
• Query caching is optional and can be disabled
NQSConfig.ini
• Is a configuration file used by the Siebel Analytics Server at startup
• Specifies values that control processing, such as:
• Defining the repository (.rpd) to load
• Enabling or disabling caching of results
• Setting server performance parameters
DBFeatures.ini
• Is a configuration file used by the Siebel Analytics Server
• Specifies values that control SQL generation
• Defines the features supported by each database
Log Files
• NQSServer.log records Siebel Analytics Server messages
• NQQuery.log records information about query requests
Siebel Analytics Scheduler
• Manages and executes jobs requesting data analytics
• Schedules reports to be delivered to users at specified times
• In Windows, the scheduler runs as a service
Physical Layer
• Is the metadata that describes the source of the analytical data
• Defines what the data is, how the data relates and how to access the data
• Is used by the Siebel Analytics Server to generate SQL to access the business
data to provide answers to business questions
• Is created using the Analytics Administration Tool. Can be imported from the
source information.
• Is typically the first layer built in the repository.
Connection Pool
• Specifies the ODBC or native data source name
• Defines how the Siebel Analytics Server connects to the data source
• Allows multiple users to share a pool of database connections
• May create multiple connection pools to improve performance for groups of
users
Creating Dimension Levels and Keys:
• A dimension contains two or more levels.
• The recommended sequence for creating levels is to create a grand total level
and then create child levels, working down to the lowest level.
• Grand total level. A special level representing the grand total for a
dimension. Each dimension can have just one Grand Total level. A grand total
level does not contain dimensional attributes and does not have a level key.
• Level. All levels, except the Grand Total level, need to have at least one
column.
• Hierarchy. In each business model, in the logical levels, you need to
establish the hierarchy (parent-child levels). One model might be set up so
that weeks roll up into a year.
• Level keys. Each level (except the topmost level defined as a Grand Total
level) needs to have one or more attributes that compose a level key. The level
key defines the unique elements in each level. The dimension table logical key
has to be associated with the lowest level of a dimension and has to be the
level key for that level.
Associating a Logical Column and Its Table with a Dimension Level
After you create all levels within a dimension, you need to drag and drop one
or more columns from the dimension table to each level except the Grand Total
level. The first time you drag a column to a dimension it associates the
logical table to the dimension. It also associates the logical column with that
level of the dimension. To change the level to be associated with that logical
column, you can drag a column from one level to another.
After you associate a logical column with a dimension level, the tables in
which these columns exist appear in the Tables tab of the Dimensions dialog
box.
To verify tables those are associated with a dimension
1. In the Business Model and Mapping layer, double-click a dimension.
2. In the Dimensions dialog box, click the Tables tab.
The tables list contains tables that you associated with that dimension. This
list of tables includes only one logical dimension table and one or more
logical fact tables (if you created level-based measures).
3. Click OK or Cancel to close the Dimensions dialog box.
Defining a Non Aggregated Measure of a Fact Table
Two methods to do this
Method 1:
• Find any dimension logical table is available to add these filed
• If so add these fact table as source to existed dimensional logical
table
Method 2:
• If there is no logical dimensional table
• Create new logical table
• Make the source as Fact table
• Create a Dimensional hierarchy to the new logical table
• In business model diagram create a complex join between the dimension logical
table and the fact logical table
• Also create a complex join to any other fact logical table mapped to the same
physical table
Defining an Aggregated Measure of a Dimension Table:
1) Create new Fact Logical Table
2) Dimension Table as source table for the new Fact logical table
3) Include the logical columns that should be a measure of fact table.
If aggregated calculations are performed directly from a dimension logical
table field, an error similar to the following will appear:
A general error has occurred. [nQSError: 14026] Unable to navigate requested
expression: ). Please fix the metadata consistency warnings.
To resolve this type of error, put the measure indicated by the error message
in a fact table object.
OBIEE?
Oracle Business Intelligence Enterprise Edition
Note :
Job Able to see Online Mode
Cache
Session
Project Contains ?
Presentation Catalogs ,
Logical Fact Tables,(Can able to see only Logical Fact Table , No Dimension
tables and Hierarchies )
Variables,
Groups,
Users ,
Initialization Blocks
Where we will use Projects ?
We will use the projects in Multi-user Development Environment .
Where Primary Key and Foreign Key available ?
PK and FK are available in Physical and Logical Tables.
Can we crate Physical column for alias Table ?
No we cant create .
we can create only for Physical table
Use of Alias table ?
To avoid Circular joins
Situation where we have to see same table more than once
Base Line Column ?
Is a column that has no aggregation Rule defined in Aggregation Tab of Logical
Column
Base line column map to non-aggregated Data at the level of Granularity of
logical source
Case 1: If there is no GROUP BY clause specified, the level of aggregation is
grouped by all of the nonaggregate columns in the SELECT list.
select year, product, sum(revenue) from time, products, facts Group By will be
happened in year and Product
Case 2 :
If there is a GROUP BY clause specified, the level of aggregation is based on
the columns specified in the GROUP BY clause.
select year, product, sum(revenue) from time, products, facts group by year,
product
Offline Mode ?
RPD is not loaded in to SAS server
RPD opens in Read only Mode
At a time only one admin tool session will be editable after restart SAS then
only saved changes will be reflect to UI
Online Mode?
RPD Loaded in to SAS Server
After Check in and Save by click on the ‘Reload Server Metadata ‘ will display
the saved changes without SAS server
Load all Objects on Start up ?
this option available only in Online mode
This loads all objects immediately, rather than as selected. The initial
connect time may increase slightly, but opening items in the tree and checking
out items will be faster
Data Source name (DSN ) in Online open Rep dialog box?
AnalyticsWeb is DSN.This Option available in only in Online mode
From above we need to select DSN. We can able to all User and System DSN which
are configured using SAS (Oracle BI ) ODBC Driver.This DSN we have to config in
SAW (10.195.120.48)… and provide data for the following option
‘Which SAS Server DO we need to Connect to “ ---- SAS(10.195.120.49)
To configure Siebel Analytics Web installed on a different machine from the
Siebel
Analytics Server
1 On the machine where Siebel Analytics Web is installed, modify the odbc.ini
file (located in the folder $INSTALLDIR/setup) as follows: [AnalyticsWeb]
Driver=[client $INSTALLDIR]/Bin/libnqsodbc.[$libsuffix]
NOTE: The string [$libsuffix] represents the library suffix appropriate to the
specific UNIX
operating system you are using.
For example, for Solaris or AIX, use libnqsodbc.so; for HP-UX, use
libnqsodbc.sl.
Description=Siebel Analytics Server
ServerMachine= Port=
2 Save and close the file.
Consistency Check Manager can provide following types of messages ?
Error Messages
Warning Messages
Best Practices
Check Consistency levels ?
Repository level
Object Level ( in 3 layers )
What is the use of “ Options -> Display qualified names in diagrams”?
Before Check :
After Check
What is the use of “Tools ->Option -> Allow import from repository “ ?
By this “Import from the repository” on file menu will be available
it is recommended to create Projects and use this option while Merge .
Use of Display Folders ?
To organize the objects in Physical and Logical Layer
For this No Metadata Meaning
Selected objects appears in this folder as shortcut and In BMM Or Physical
Layer as Objects
we can hide the Objects in BMM and Physical Layer so that only shortcuts will
be visible.
Update Row Count is in 2 Ways ?
Update row count is possible
Table Level
Column Level
Update Row count is not possible in following Scenarios ?
SP Object Type
XML Data Source
Multi Dimensional Data Source
In Online mode if Connection Pool uses following Session Variable
User name : USER and
Password :PASSWORD
In Online mode after Importing or Manually creation of tables and columns –
After check in only Update row count will be available
Use of Level Counts ?
Level counts are utilized by the Query Engine to determine the most optimal
Query plan and Optimize the overall system Performance
Types of Physical Schemas?
E-R Schema
Dimensional Schema
Types of Dimensional Schema?
Star Schema
Snow flake Schema
Note :In Snowflake schema one or more Dimensions are Normalized to some
extent
RPD Contains what ?
SAS or OBI Server stores Metadata in Repository
Tips while designing Physical Layer ?
Before Import from DW Eliminate all outer joins
Import Physical Data without PK and FK
Tips while designing BMM layer ?
Create BMM layer with 1:N Complex Join between Dim – Fact tables .
Every Dim Table associated with Dim Hierarchy
All Fact Sources links to Proper level in the Hierarchy using Aggregation
Content
Use Alias table to eliminate Circular Joins
Physical Layer
What is the use of “Allow Direct Database Request By default ?”
This property allow all users to execute Physical Queries
What is the use of “Allow Populate Queries By default ?”
It will allow to execute POPULATE SQL
SQL Features ?
These SQL Features will automatically populate with default values of database
types.
EX: if Data source supports left outer join but we want to prohibited the SAS
server to from sending such queries to particular data base , then we can
change the default settings in features table .
Connectionpool -> Enable Connection Pooling ?
Single Database connection remain open for Specified time for further query
usage
So by this Open and crate for new connection for every request will be
reduced.
Persists Connection Pool Property ?
To use this property we must use Temp table first.
This is a database Property .and it is used for specific type of Queries
Ex: In some queries all of the logical query cannot sent to Transactional DB
because that DB may not support those functions which used in Query. This might
be solved by temporarily creating table in DB and rewriting the SAS server to
reference new temp Table
Persistent connection pool will give change to write back option. if this was
enabled User name specified in connection pool have privileges to create DDL
and DML in DB
Use Default Specific SQL?
For Table Type
Stored Procedure
Select
Need to select above check box.
If select : at run time SP or Select Statement has been defined the SP or
Select statement has been executed
If not Selected : Default configurations will be executed
Where we can give 1:1 relation ?
We can give the 1:1 relation to Dim and Mini or Dim to Dim Extn Tables
Bridge Table?
If required Many-to-Many relation between Dimension and Fact we have go for
Bridge table
We can create a bridge table that resides between the fact table and the
dimension table.
Bridge table stores the Multiple records corresponding to Dimension Table.
Fact Bridge Dimension
for each patient admission, there can be multiple diagnoses.
Example,
a patient can be diagnosed with the flu and with a broken wrist.
The bridge table then needs to have a weight factor column in it so that all of
the diagnoses for a single admission add up to a value of 1.
The weight factor has to be calculated as part of the process of building the
data.
For the case of the patient diagnosed with the flu and a broken wrist, there
would be one record in the Admission Records table, two records in the
Diagnosis Record table, and two records in the Diagnosis table,
Deleting Physical Table ?
When we delete Physical table all dependent objects will be deleted .
Note: View Data ?
View data willnot be possible if we use the User : USER Password : PASSWORD
session variable for the Connection pool .
Hierarchy in Physical Layer?
This is possible for Multidimensional Data Source. I.e. adding Hierarchy to
Physical Cube Table.
Catalog Folder ?
Catalog Folder contains one or more Schema Folders .
Catalog folders are optional folders in Physical Layer
Schema Folder ?
Schema Folder contains tables and Columns.
Schema folders are optional .
Usage of Variable to specify name of Catalog and Schema ?
We can use variable to specify name of Catalog and Schema objects .
Ex : we have data for Separate Clients .
Can creates separate Catalog for each separate client
For this crated Session variable named Client
This could be used to set the name of the client Dynamically when user signs to
SAS
Display Folder in Physical Layer ?
To Organize the Table objects in Physical Layer .
No metadata meaning
Selected Tables appear in the folder as shortcut and also Physical Layer tree
as objects .
We can hide the Objects as physical tree so Short cut only visible in Display
folder
Notes : Joins ?
Imported Physical and Foreign Key joins are do not used in meta data
Notes Joins ?
There is possible of join between Multiple Database . ie table under one
database can join with table under another database
But this is significantly slower than Join between 2 tables in same DB.
Fragmented Data ?
Data from a single domain that split between different tables
a database might store sales data for customers with last names beginning with
the letter A through M in one table and last names from N through Z in another
table. With fragmented tables, you need to define all of the join conditions
between each fragment and all the tables it relates to.
Complex join ?
It is non PK-FK join .
Physical Layer Expression is Possible
No Cordiality
BMM Layer No Expression
Cordiality is possible
Physical and Logical Foreign Key Join ?
In Both Physical and BMM layer Expression is Possible but not the
cordinality
It is always 1:N
Opaque View?
Physical Layer table that consists of Select Statement. Opaque view appears as
View in Physical layer but it doesn’t exist actually. Need to deploy opaque
view using Opaque Utility After Deployed it is called Deployed View .It can be
used with out deployed but SAS server generates more complex query when this
view encountered XLS and Non-Relation DB doesn’t support this feature .
Make sure CREATE_VIEW_SUPPORTED SQL feature should select in DB dialog Box
Deploying Opaque View Utility available in Offline.
Driving Table?
It is available in BMM Layer in Both Logical Foreign Key Join and Logical Join
(In
Physical Layer it is Disabled) It is used in where SAS server processes Cross –
DB Joins when One table is very small (Driving Table) and another table is very
Big. Driving tables can be used with Inner Joins. For outer Join , if it Left
outer join Driving table is Left table , if it Right outer join Driving table
is Right table What are the 2 entries (Performance Tuning Parameter ) in DB
features table that control and Tune driving table Performance ?
MAX_PARAMETERS_PER_DRIVE_JOIN
MAX_QUERIES_PER_DRIVE_JOIN
Above parameters available in C:\OracleBI\server\Config\DBFeatures.INI file
Database Hints ?
Database hints are instructions that are placed with in SQL Statement which
tells the DB Query optimizer the most efficient way to execute the statement .
Hints override Optimizer execution plan Hints are DB specific.It is available
only for Oracle 8i,9i,10g server
Note :In Physical Layer DB -> General ->If the Database type is
Oracle
Then only we can find HINT option in Table General Properties
For alias table Hint will be in disabled state
Caching for Alias table ?
By default it will be Disabled If we select “Override Source table Caching
Properties “ then Options will be in enabled state
BMM Layer :
Complex join in BMM Layer ?
In BMM we use complex join to establish to which logical tables are joined with
which table ?
SAS server goes to Physical layer to search Physical join to make Query.
We can also set Complex join in Physical layer but SAS won’t be able to
construct Physical Query
BMM -> Table -> Property->Source ->Edit (Add) -> Content ->
Aggregation Content Group By ?
If we select Logical Level .The Group by ( Aggregation ) will be at the
Dimension Hierarchy (Month,Year,Week etc) level will be happen
If we select Column the Group by (Aggregation) will be at the Table->Column
Level
Note: Do not mix aggregation by Logical Level and Column Level in same Business
model .
It is recommended to use Logical Level
Logical Primary Key ?
Logical Primary key must have for Logical Dimensional Table . and Optional for
Logical Fact table .
Logical Foreign Key ?
Do not create foreign key for Logical Tables.
Default Aggregation Rule?
Is Count Distinct
Grand Total Level ?
Each Dimension will have 1 Grand Total. It doesn’t contain Level key and
Attributes.
Preferred Drill Path ?
To identify Preferred drill path to use when SAW user to drill down their data
request .
Use this feature to specify a drill path that is used outside of normal drill
path defined by Dimensional Hierarchy.
This is commonly used to drill from one Dimension to Another Dimension (Select
the level from Current Dimension or other Dimension)
Creating Dimension Automatically?
Can create Dimension Automatically from Logical Dimension Table if Dimension is
not existed.
Dimension Specific Aggregation?
Mostly Measures have Same Aggregation for each Dimension .Ie bank balances
might be averaged over time but summed over the individual accounts .SAS allows
Dimension Specific Aggregation.
Can we provide Aggregation for Multiple rows at a time ?
Yes
Logical Joins In BMM Layer ?
Logical Join are nothing But Complex joins
Logical Tables are related to each other . how they are related is expressed in
Logical Joins .
Key properly of Logical Joins is Cordiality
Cardinality express how rows in one table are related rows in second table .
Logical Table joins are required so that SAS can have necessary metadata to
translate Logical Request against the BMM layer to SQL Queries against Physical
Data source
In BMM layer we should create only Complex joins one –To-many Relation and not
any FK join .
The Existance of Physical join doesn’t require machining join in BMM
Layer
Usage of Logical Foreign Keys ?
Logical Foreign Key Join may be needed if SAS server is to be used ODBC data
source for certain third party query and Reporting tool
Presentation Layer:
Column Alias Name ?
Whenever if we change the name of the Presentation column name an alias is
automatically created for the Old name , So compatibility to the old name
remains .
Note : Alias is available for Presentation catalog
Presentation Table
Presentation Column
Presentation Catalog ?
The contents of Catalog can be populated only from Single Business Mode. Can
not span Business Models.
Nested Folders in Answers ?
Prefix the name of the presentation folder to be nested with a hyphen and a
space and place it after the folder in which it nests (- ).
Presentation Column Name ?
By default Presentation column name if identical to BMM Layer column Name.
However we can give different column name be uncheck
‘Use Logical Column name ‘
‘Display Custom Name’
Availability of “Permissions Tab “?
It is available in
Presentation Ctalog
Presentation Table
Presentation Column
Variables :
Repository Variable ?
Has Single value at any point of time .
Static
Dynamic
Session variable ?
Created and assigned a value when each user logs on.
Initialization Block?
It is used to initialize Dynamic ,Session non System variables.
Where can use Static Repository variables ?
Variables can be used instead of Literals and Constants in Expression Builder
in tool.
Ex:
CASE WHEN "Hour" >= 17 AND "Hour" < 23 THEN 'Prime
Time' WHEN... ELSE...END
CASE WHEN "Hour" >=VALUEOF(“VAR1”) AND "Hour"
Dynamic Repository Variable ?
It is same as Static variable . but values are refreshed by data returned from
queries .
For this need to use Initialization block which execute SQL Query
An also schedule that the SAS will refresh the value of variable
periodically
Session variables ?
These are similar to Dynamic Variables. But this will not Scheduled.
Unlike repository variable, this will have many instances
Non System Session Variable ?
It is same as session variable .
Common use of this is setting User Filters .
Ex: Create non System variable called Sales Region
This would be initialized to name of users Sales Region
So we can set security filter for all members of group would allow them to view
only data related to their region.
Session variable -> Enable Any User to Set the Value ?
Allow to set the value of variable after Initialization block has populated the
value by calling ODBC SP NQSetSessionValue()
What is NQ_SYSTEM session variable ?
It is initialization block is used to refresh system session variable .
Session variable -> Displayname
Is used to display in the UI “Welcome Swapna”
If we not provide Displayname session variable and login the app with v-swapns
, it will display as “Welcome v-swpns”
Because Displayname use the initializationblok -> Login Properties (Select
P.NAME
from VALUEOF(TBO).S_PARTY P, VALUEOF(TBO).S_USER U
WHERE U.LOGIN=':USER' AND U.PAR_ROW_ID=P.ROW_ID’)
Row-wise Initialization?
It allow to create session variable dynamically and set their values when
session starts .
Name and value of session variable reside in external table that access through
connection pool
Create the session variables using values contained in table XXXXX
Contains the columns
USERID: Represents user unique Identifier
NAME: Represent Session variable Name
VALUE: Represents the Session variable Value
Create Initialization Block and Select Row-wise Initialization check box.
Select NAME ,VALUE from XXXXX where USERID= ‘VALUEOF(NQ_SESSION.USERID)’
Here NQ_SESSION.USERID is already initialized another initialization
block
When JOHN log in his session contain 2 session variable (LEVEL , STATUS)
When JANE los in his session contain 3 session variables (LEVEL , STATUS,GRADE)
Dedicated Connection for initialization block ?
Create Dedicated Connection for initialization block .
Value of Repository variable ?
When we open Rep in Online mode the value of variable is which we defined a
default value.
Note : If number of variables are differ from number of columns …..then
If variables are less than columns then Extra column values are ignored .
If variables are more than columns then additional variables are not
Refreshed
Notes on Row – Wise initialization ?
For session variables initialization block we can create this
Initialization Block -> Execution Precedence?
If REP contains more than one Initialization block, we can set the order in
which block will be initialized.
Ex : we have A and B .
Open B and Specify A will be execute before B
Setting Up Aggregate Navigation:
Use of Where clause Filter in Logical table -> Source -> Content ?
It is used to Limit or Restrict the Physical Table that is referenced in
logical table source .
If there is no Limit , leave that as blank .
Each logical table Source Should contains data at single aggregation level
.should not create a source that had the sales data at both Brand and
Manufacturing levels.
If Physical table include date at more than one level add appropriate where
clause limit to filter values to single level .
Any limit in where clause filter are made on the Physical table in source .
Use of Fragment Content in Logical table -> Source -> Content ?
If logical table doesn’t contains entire set of data at given level, need to
specify the Portion or Fragment.
Describe the content in terms of logical columns.
Fragment1:
Logical column IN
Fragment1:
Logical Column IN
Security:
Usage of Filters?
Use filters to limit data accessible by user.
User?
User accounts can be defined explicitly in SAS , External DB and LDAP.
Grant permission rights?
We can grant rights permission to user individual , group , or combination of
both .
Creation of user?
After creation of user , it will have default rights was granted .
In NQSConfig.ini , the default rights are specified by DEFAULT_PREVILAGES
Administrator Account ?
We can’t delete or modify other than Login level and Password change
Can set Password min length in NQSConfig.ini file using
MINIMUM_PASSWORD_LENGTH
User Privileges?
Users can have explicitly granted Privileges, and also through Groups.
Privileges Hierarchy?
Privileges granted explicitly to Users have Priority over Privileges granted
through Group
user will have Read Permission on Table A
Privileges granted explicitly to Group have Priority over Privileges granted
through other Group
User will have read Privileges on table A ,B,C
Note : Group 1 and Group 2 are in same level in this case Less Restrictive
level will be takes place (Deny , Read = Read)
LDAP V/S Repository Security?
If we create variable for same user in both REP and LDAP, then local REP user
definition will take priority and LDAP authentication will not occur.
Authentication
Authentication?
It is a process to check the user has necessary permissions and authorizations
to login to application and access data
Authentication types?
OS
LDAP
External Table
Database
SAS user Authentication
OS Authentication?
It is only for ODBC client Application not for SAW.
It is only for login to SAS client
LDAP?
Lightweight Directory Access Protocol.
Along with user authentication, it also contains
Display name,
user belongs to which group
Name of DB catalogs and Schema
External table?
Along with user authentication, it also contains
Display name ,
user belongs to which group
Name of DB catalogs and Schema
External table Authentication can be used in conjunction with Database
authentication .
DB authentication ?
If user have read permissions on specific DB then user will trusted by SAS
server .
Unlike OS authentication this can be applied to SAW also.
Bypassing(Avoiding) Siebel Analytics Security?
We have option in NQSConfig.ini file
AUTHENTICATION_TYPE=BYPASS_NQ
Caching :
Ways to Purge the cache ?
Manually, using the Administration Tool Cache Manager facility (in online
mode).
Automatically, by setting the Cache Persistence Time field in the Physical
Table
Event polling table.
Automatically, as the cache storage space fills up.
Initializing cache entry for User ID?
To do this , the connection pool need to be setup for shared login with session
variables USER and PASSWORD
Cache Storage gets filled up ?
Then LRU are discarded and make space for new entries
Max Cache values?
If number of rows returned by Query is more than the value specified in
‘MAX_ROWS_PER_CACHE_ENTRY’ parameter then Query will not be cached.
Event Pooling Tables ?
This tables store the information about updates in underlying DB
Create the table with following Schema (Database name ,Catalog name , Schema
Name, Table Name , Other ,Update Time ,Update Type)
To mark the table object as an Event Polling Table
1. Click on the Tools > Utilities menu item.
2. Select the option Oracle BI Event Tables from the list of options.
3. Click Execute.
4. Select the table to register as an Event Table and click the >>
button.
5. Specify the polling frequency in minutes, and click OK.
The default value is 60 minutes.
NOTE: You should not set the polling frequency to less than 10 minutes. If you
want a very short polling interval, consider marking some or all of the tables
non-cacheable.
Disabling Caching?
Disabling cache for whole system can done in NQSConfig.ini by ENABLE = NO . and
Restart SAS.
Disbling cache will do
Stops all new cache entries .
Stops new quires from Existing cache
Disabling cache can be enabled without losing any entries already stored in
cache
Purge Cache Programmatically ?
Call SAPurgeCacheByQuery ('select lastname, firstname from employee where
salary > 100000’);
Call SAPurgeCacheByTable('DBName', 'CatName', 'SchName', 'TabName' );
Call SAPurgeAllCache();
Call SAPurgeCacheByDatabase( 'DBName' );
Nulls passed as input parameters to SAPurgeCacheByTable serve as wild cards.
For example, specifying a database name but leaving the catalog, schema and
table names null will direct the function to purge all entries associated with
the specified database.
Cache Hits ?
For cache hits , it should follows some conditions .
Make changes to Repository ?what will be happen when changes occur in
Online,Offline and Switch Btw Rep?
Online Mode :
If we change any object , cache related to that changed object will be Purged
automatically.
Any changes made to BMM will purge the all cache entries for the BMM layer .
Purge occurs when check in will takes place
Offline Mode :
In Offline purge will not happen automatically.
Switch Btw Rep:
Before Switch btw repositories Purge the cache and then switch to another
Purging cache ways?
Manually using Admin tool
Cache Persistence Time in Physical tables
Event Pooling Table
Automatically cache storage fills up
Administering the Query Environment:
What NQServer.log file contains ?
Start up time
Business model that are started
Errors if any occurred .
Controlling size of NQQuery .log file ?
The parameter USER_LOG_FILE_SIZE in NQSConfig.INI file determines the size of
the NQQuery.log file.
When the log file grows to one-half the size specified by the
USER_LOG_FILE_SIZE parameter, the file is renamed to NQQuery.log.old, and a new
log file is created automatically.
Only one copy of the old file is kept.
If you change the value of the USER_LOG_FILE_SIZE parameter, you need to
restart the Siebel Analytics Server
Enabling Logging Level ?
It is possible to enable Logging level for users
Not for Group .
Logging levels greater than 2 should be used only with the assistance of Siebel
Technical Support.
Usage Tracking ?
We can enable this in NQSConfig.ini file
ENABLE = YES;
Setup and Managing Repository:
Import Repository ?
To enable this Tools->Options->General
Will work in Offline Mode .
Comparing Repositories ?
It will compare 2 repositories .
Compare ur customized rep to your new version of Repository.
It will be work in Offline Mode .
Steps:
Open Rep in Offline . this rep is Current Rep
File->Compare
Select Original Rep Dialog Box->Select Rep which we require to compare.
Use compare rep Dialog Box
Merge Repositories ?
This option is used to upgrade the Custom Rep
This process involves 3 versions of Rep.
Original Previous Version of Rep (Like Dummy Rep 1st Rep)
Modified Customizations that modified to Original Rep (This is the rep whose
objects would like to copy to current rep)
Current Installed with this Version and Currently Opened as Main Rep(Like 3rd
Rep)
During this Merge Process we can compare with
Original To Modified
Original To Current
we have 2 rep with their own Phy,BMM,Pre layers
use Merge Option to Merge above 2 rep to 3rd Rep.
1+2 = 3
Ex : We have Paint Rep
Another is UsageTracking Rep
Our aim to get usageTracking Rep to Paint Rep
Projects ?
Projects consists of subset of metadata
Its contains Catalogs and associated BMM objects(Fact Tables Only ) , Groups,
Users , variables and Initialization Blocks
Usage of Projects ?
Mostly we will use in Multi User Development (MUD)
Only one can create Projects in master Rep
Multi User Development?
Need to work Concurrently on subset of metadata and Merge those into master
Repository.
IMP Steps: Admin create Projects
Rep Copied into Shared N/W path
Developers checkout their Projects
Total Steps; Admin create Projects
Rep Copied into Shared N/W path
Before Checkout Developer must points Admin tool to Shared path
Checkout rep Projects
Multi-user -> Checkout
Compare with Original (Compare Working Extracted Local Rep to Original Rep)
Merge Local Changes (Locks Master Rep to allow you to check in changes)
Or Discard Local Changes (Any time After Checkout and Before Check in can
discard changes)
Publish To Network (After Successfully Merge, Master Rep open local and This
Item’ll be available. After select this option lock is removed Rep is Published
and rep will be closed)
Only one developer at a time can merge metadata from Local Rep into Master Rep.
Other :
Calculation Wizard ?
To Create new calculation column that compare 2 existing columns and to created
metric in Bulk(Along with Aggregation )
Start this wizard under BMM Layer -> Logical Column (Right Click)with data
type Numeric.
Hierarchy Dimension -> Number of Elements at this Level ?
Number of elements at this level to 3. This number does not have to be exact.
The ratio from one level to the next is more important than the absolute
number. These numbers only affect which aggregate source is used (optimization,
not correctness of queries).
Case sensitive Option ?
CASE_SENSITIVE_CHARACTER_COMPARISON = OFF
In NQSConfig.ini
Siebel Analytics Server :- It generates dynamic SQL to query data in the data
sources. The Siebel Analytics Server user IDs are stored in non-encrypted form
in a Siebel Analytics Server repository and are case insensitive. Passwords are
stored in encrypted form and are case-sensitive.
Siebel relationship management warehouse(SRMW):- It is a database that contains
the data extracted, transformed and loaded from Siebel eBusiness Applications.
Siebel analytics scheduler :- Schedules reports to be delivered to users at
specified times.
NQQuery.log :- Records query requests.
Siebel Analytics Web server :- It receives data from the Siebel analytics
server and provides data to the client that requested it.
Clients :- Provides the interface to access the data.
Siebel Delivers :- It automates requests that have been created and saved with
Siebel Answers.
Repository File(.rpd) :- Contains metadata that represents the analytical
model.
NQSServer.log :- Records Siebel analytics server messages.
NQSConfig.ini :- Configuration file used by Siebel analytics server at start
up.
.webcat :- Stores application dashboards, request definitions, pages and
filters.
Datasources :- Contain the business data users want to analyze.
Pivot Table :- The Pivot Table view allows you to take row, column, and section
headings, and swap them around to obtain different perspectives of the data.
Funnel Chart:- The Funnel Chart view displays a three-dimensional chart
representing target land actual values using volume, level and color.
Ibots:- Siebel Delivers uses intelligence agents called ibots. iBots provide
delivery of real-time and personalized analytics alerts throughout your
organization’s network.
Siebel Alerts:- The Siebel Alerts page shows your currently active alerts,
along with information about when the content was delivered. When alerts are
present, the link Alerts! appears at the top of each Siebel Answers, Siebel
Delivers, and Siebel Intelligence Dashboard page.
Global filters:- They act as an independent control for the entire dashboard,
and can update any report on that dashboard that shares columns with the global
filter.
Query Caching:- The query cache in Siebel Analytics Server is a facility that
stores the results from queries. It is used for improvement of query
performance, less network traffic.
Repository Variables:- A repository variable has a single value at any point in
time. There are two types of repository variables: static and dynamic.
Repository variables are represented by a question mark icon.
Static variable: The value of a static repository value is initialized in the
Variable dialog box. This value persists, and does not change until a Siebel
Analytics Server administrator decides to change it.
Dynamic variable: You initialize dynamic repository variables in the same way
as static variables, but the values are refreshed by data returned from
queries. When defining a dynamic repository variable, you will create an
initialization block or use a preexisting one that contains a SQL query. You
will also set up a schedule that the Siebel Analytics Server will follow to
execute the query and periodically refresh the value of the variable.
Session Variables:- Session variables are created and assigned a value when
each user logs on. If a user is authenticated successfully, session variables
can be used to set filters and permissions for that session. There are two
types of session variables: system and non-system. System and non-system
variables are represented by a question mark icon.
System Variables: System variables are session variables that the Siebel
Analytics Server and Siebel Analytics Web use for specific purposes. System
variables have reserved names, which cannot be used for other kinds of
variables. When using these variables in the Web, preface their names with
NQ_SESSION.
Non-system Variables: The procedure for defining non-system session variables
is the same as for system session variables. When using these variables in the
Web, preface their names with NQ_SESSION. A common use for non-system session
variables is setting User filters.
Initialization Blocks:- An initialization block contains the SQL that will be
executed to initialize or refresh the variables associated with that block.
Initialization blocks are used to initialize dynamic repository variables,
system session variables, and non-system session variables. (The NQ_SYSTEM
initialization block is used to refresh system session variables.)
Stand-Alone Siebel Analytics (Siebel Analytics Server)The stand-alone
configuration involves the Siebel Analytics Server only. You must develop your
own analytics applications and configure them to connect to legacy data
warehouses or other data sources.
Integrated Siebel Analytics (Siebel Analytics applications)You can configure
Siebel Analytics to run with Siebel eBusiness Applications and with Siebel
Industry Applications to use the Siebel Data Warehouse or pre-built (and
sometimes specialized) data warehouses.
Security:- The Siebel Analytics Server and Web client support industry-standard
security for login and password encryption. When an end user enters a login and
password in the Web browser, the Siebel Analytics Server uses the Hyper Text
Transport Protocol Secure (HTTPS) standard to send the information to a secure
port on the Web server. From the Web server, the information is passed through
ODBC to the Siebel Analytics Server, using Triple DES (Data Encryption
Standard). This provides an extremely high level of security (168 bit),
preventing unauthorized users from accessing data or analytics metadata. The
Siebel Analytics Server Administrator account (user ID of Administrator) is a
default user account in every Siebel Analytics Server repository. This is a
permanent account. When you create a new repository, the Administrator account
is created automatically and has no password assigned to it. It cannot be
deleted or modified other than to change the password and logging level. It is
designed to perform all administrative tasks in a repository, such as importing
physical schemas, creating business models, and creating users and groups.
Authentication:- Authentication is the process, by which a system verifies,
through the use of a user ID and password, that a user has the necessary
permissions and authorizations to log in and access data.
OS Authentication:- Users with identical Windows and Siebel Analytics Server
user IDs do not need to submit a password when logging in to the Siebel
Analytics Server from a trusted domain. When operating system authentication is
enabled, users connecting to the Siebel Analytics Server should not type a user
ID or password in the logon prompt. If a user enters a user ID and (optionally)
a password in the logon prompt, that user ID and password overrides the
operating system authentication and the Siebel Analytics Server performs the
authentication. NOTE: Operating system authentication cannot be used with
Analytics Web. It can only be used with ODBC client applications.
LDAP(Lightweight Directory Access Protocol) Authentication:-It is used for
hierarchical data access.To configure LDAP authentication, you define a system
variable called USER and associate it with an LDAP initialization block, which
is associated with an LDAP server. Whenever a user logs into the Siebel
Analytics Server, the user ID and password will be passed to the LDAP server
for authentication. After the user is authenticated successfully, other session
variables for the user could also be populated from information returned by the
LDAP server.
Database Authentication:- The Siebel Analytics Server can authenticate users
through database logons. If a user has read permission on a specified database,
the user will be trusted by the Siebel Analytics Server. NOTE: Siebel Delivers does
not work with database authentication.
Mini Dimension Tables:- contains the combination of most frequently queried
attributes.
Aggregate Tables:- Aggregate tables store pre-computed results — measures that
have been aggregated (typically summed) over a set of dimensional attributes.
Using aggregate tables is a very popular technique for speeding up query
response times in decision support systems
About Dimensions and Hierarchical Levels
In a business model, a dimension represents a hierarchical organization of
logical
columns (attributes) belonging to a single logical dimension table. Common
dimensions might be time periods, products, markets, customers, suppliers,
promotion conditions, raw materials, manufacturing plants, transportation
methods, media types, and time of day. Dimensions exist in the Business Model
and
Mapping (logical) layer and end users do not see them.
In each dimension, you organize attributes into hierarchical levels. These
levels
represent the organizational rules, and reporting needs required by your
business.
They provide the structure (metadata) that the Siebel Analytics Server uses to
drill
into and across dimensions to get more detailed views of the data.
Dimension hierarchical levels are used to perform the following actions:
■ Aggregate navigation
■ Configure level-based measure calculations
(see “Level-Based Measure
Calculations Example” on page 149)
■ Determine what attributes appear when Siebel
Analytics Web users drill down
in their data requests
Message numbers are listed in the format nnxxx, where nn is the message prefix
that identifies the category of the message, and xxx is the numeric identifier
of the
message in that category.
Siebel Analytics Scheduler
Siebel Analytics Scheduler manages and schedules jobs. A job is a task
performed by Siebel Analytics
Server. Siebel Analytics Scheduler supports two types of jobs:
■ Scripted jobs that you set up and submit
using the Job Manager feature of the Server
Administration Tool
■ Unscripted jobs, called iBots, that you set up
and submit using Siebel Delivers
Siebel Analytics Complete Solution
Summary of Siebel Analytics as defined in this module:
Subject Areas
Contain information about then areas of your organization’s business
Have names that correspond to then type of information they contain
Select columns fromn subject area virtual tables in the selection
pane to create request criteria
By default, results are displayed in compound layout format, which includes the
Title and Table views
Use Save Request to save a request in a personal or shared folder
Intelligence Dashboards
n Are pages in a Siebel Analytics application
used to display:
Results of one} or more saved Siebel Analytics requests
Other content items, such as}
n Links to Web sites
ActiveX objectsn
HTML textn
Links ton documents
Embedded content: images, text, charts,
tablesn
Are providedn in Siebel Analytics applications
Can be created by Siebel Analytics usersn or application developers
Can be shared by common groups of usersn
Cann be modified based on personal preferences and business needs
Accessing Intelligence Dashboards
To access Intelligence Dashboards in the standalone version of Siebel
Analytics, select Start > Programs > Siebel Analytics > Siebel
Analytics Web
Accessing Saved Intelligence Dashboards
Select Dashboards tab to access saved
dashboards in Siebeln Answers
Provide rebuilt, fully-interactive access to analytics information
Siebel Analytics Architecture
Is made up of five mainn components:
Clients}
Siebel Analytics Web Server }
Siebel Analytics} Server
Siebel Analytics Scheduler}
Data Sources}
Siebel Analytics Web Administration
Is used to access administrative functions of Siebel Analytics Web and view
information about the installed system
Siebel Analytics Web Catalog (.webcat)
Stores then application dashboards, request definitions,
pages, and filters
Containsn information regarding permissions and
accessibility of the dashboards by groups and users
Is created when the Web Server startsn
Is specified in then registry of the machine running the Web
Server
Is administered using Siebeln Analytics Catalog Manager
Repository File (.rpd)
Contains metadatan that represents the analytical model
Is created using the Siebel Analyticsn Administration Tool
Is divided into three layersn
Physical — represents} the data sources
Business — models the data sources into
facts and} dimensions
Presentation - specifies the users view
of the model; rendered} in Siebel Answers
Cache
Contains results of queriesn
Is used ton eliminate redundant queries to database
Speeds up results processing}
n Query caching is optional
Can be disabled}
NQSConfig.ini
n Is a configuration file used by the Siebel
Analytics Server at startup
n Specifies values that control processing,
such as:
Defining the repository} (.rpd) to load
Enabling or disabling caching of results}
Setting server} performance parameters
DBFeatures.ini
Is a configuration file usedn by the Siebel Analytics Server
Specifies values that control SQLn generation
Defines the features supported by each
database}
Log Files
NQServer.log records Siebel Analytics
Server messagesn
n NQQuery.log records information about query
requests
Siebel Analytics Scheduler
Manages and executes jobs requesting
data analyticsn
n Schedules reports to be delivered to users at
specified times
In Windows,n the scheduler runs as a service
Data Sources
Contain then business data users want to analyze
Are accessed by the Siebel Analyticsn Server
Can be in any format, such asn
Relational databases}
Online} Analytical Processing (OLAP) databases
Flat files}
Spreadsheets or} other ODBC data sources
XML}
Siebel Relationship Management Warehouse
Is a predefined data source to support analysis
of Siebeln application data
Relevant data structures support Siebel
eBusiness} Applications
Is in a star schema formatn
Is included with Siebeln Analytics Applications (not available with
standalone Analytics purchases)
DAC and Informatica Server
Data Warehouse Applicationn Console (DAC) Client
Used to schedule, monitor, configure,
and customize} SRMW extraction, transformation, and load
Accesses metadata about ETL} mappings and dependencies in the DAC
repository
DAC Servern
Organizes} ETL requests for processing
Third party Informatica Server populates
then SRMW from the Siebel eBusiness Application
Database (Siebel OLTP)
Uses} extract, transform, and load (ETL) routines
Siebel RMW: Siebel Relationship management warehouse
Informatica Server ETL
Usesn Source Dependent Extraction (SDE) routines to extract data
Loads data inton staging tables within the SRMW
Uses Source Independent Loading (SIL)n routines to transform data into stars within
the SRMW
Sample Request Processing
1. User views a dashboard or submits an Answers request
2. The Siebel Analytics Web Server makes a request to the Siebel Analytics
Server to retrieve the requested data
3. The Siebel Analytics Server using the .rpd file, optimizes functions to
request the data from the data sources
4. The Siebel Analytics Server receives the data from the data sources and
processes as necessary
5. The Siebel Analytics Server passes the data to the Siebel Analytics Web
Server
6. The Siebel Analytics Web Server formats the data and sends it to the client
Siebel Analytics Standalone Architecture
Does not require any Siebel eBusiness Applications
Siebel Analytics Integrated Architecture
n Supports the Siebel Analytics Applications
Parallels the Siebel eBusinessn Applications architecture
Implementation
Siebeln Analytics components are often implemented
across several computers on the network
For example:n
Clustering Siebel Analytics Servers
n Cluster Server Feature
Allows up to 16 Siebel Analytics Servers
in a} network domain to act as a single server
Servers in cluster share requests} from multiple Siebel Analytics clients,
including Siebel Analytics Answers and Siebel Analytics Delivers
Cluster Controller is primary component
of then Cluster Server feature
Monitors status of resources in a
cluster and} performs session assignment as resources
change
Supports detection of} server failures and failover for ODBC clients
of failed servers
Data Warehousing
Brings together data from many sourcesn
Organizesn data for analytical processing
Denormalize data: Duplicate and flatten} data structures
Reduce joins: Reduce the number of
tables and} relationships
Simplify keys: Use surrogate keys such
as a sequence} number
Employ star schemas: Simplify
relationships between tables}
n Two major ways to organize data, each
optimized for different uses
} Transactional systems
Organize data to optimize transactional
throughput:n inserts, updates, and deletes
Example: Siebel transactional databasen
n OLTP
Transactional schema optimized for
read/write—multiplen joins
Analytical systems }
Organize data to optimize queries onn large datasets on separate database instance
Example: Siebel Relationshipn Management Warehouse (SRMW)
OLAPn
Analytics schema optimized forn querying large datasets—few joins
Star Schema n
Organizesn data into a central fact table with
surrounding dimension tables
Eachn dimension row has many associated fact rows
Dimension tables do notn directly relate to each other
Sales fact table with dimension tables and relationships
Contains business measures orn metrics
Data is often numerical}
Is the central table in then star
Contains attributes or characteristics
about the businessn
} Data is often descriptive (alphanumeric)
Qualifies the fact datan
n Is a technique for logically organizing
business data in a way that helps end users understand it
Data is separated into facts and
dimensions}
Users} view facts in any combination of the dimensions
Allows users to answern “Show me X by Y by Z” type questions
Example: Show me sales by product by} month
Siebel Analytics is sold in two
varietiesn
Siebel} Analytics standalone
Siebel Analytics Applications}
Access Siebel datan only (CRM Edition)
Access Siebel and/or other data
(Enterprisen Edition)
Siebel Analytics Standalone
Provides a platform to model datan so users can understand it
Provides server to generate SQL and
seamlesslyn access and manipulate data from multiple
sources
Provides a simple to use,n highly interactive, Web-based analysis tool
and the ability to pre-construct dynamic reports and alerts
Siebel Analytics Applications
Provides alln that the standalone application does, plus:
Applications for common} industry analytical processing such as
Service Analytics, Sales Analytics, Pharma Analytics, and so on
Prebuilt role-based dashboards to
support the} needs of line managers to chief executive
officers
A prebuilt database} (Siebel Relationship Management Warehouse)
designed for analytical processing with prebuilt routines to extract, load, and
transform data from the Siebel eBusiness application (transactional) database
Siebel Intelligence Dashboards
Siebel Answersn
Siebel Deliversn
Siebeln Analytics Server and Siebel Analytics Web
Siebel Relationship Managementn Warehouse (SRMW)
Siebel Analytics Administration Tooln
Siebel Answers
On-demand user interface to analytical information
Is the Siebel Analytics user interface used to query an organization’s data
Provides a set of graphical tools to create and execute requests for
information
To access the standalone version of Siebel Answers, select Start > Programs
> Siebel Analytics > Siebel Analytics Web
Which calls http://loaclhost/analytics/saw.dll?answers
Provides an self-service analysis platform
Is rendered from information in the
Siebeln Analytics Server and Siebel Analytics Web
Server
Siebel Delivers
n Platform to launch jobs and proactively
deliver results to users
Scheduled} intelligence Bots (iBots)
Proactive delivery of real-time,
personalized,} and actionable intelligence via Web, wireless, mobile, and
voice
n Capabilities and content tailored to the
device
Client applicationn that:
Is used to create iBots}
Delivers alerts to subscribed users}
} Is integrated with Dashboards and Answers
Job identifies what informationn to filter, when it should run, and who to
send alerts to
Siebel Analytics Server and Siebel Analytics Web Server
Services that access datan and return results to the user
Determine appropriate source, generate
SQL,n and merge and sort as necessary
Siebel Analytics Web Server
Provides the processing to visualize the
information for clientn consumption
Is implemented as an extension to a Web
server}
Uses the} web catalog file (.webcat) to store aspects
of the application
Receivesn data from the Siebel Analytics Server and
provides it to the client that requested it
Siebel Analytics Server
Provides efficientn processing to intelligently access the
physical data sources and structures the information
Uses metadata to direct processing }
Generates dynamic SQL} to query data in the data sources
Connects natively or via ODBC to the} RDBMS
Structures results to satisfy requests}
Merges results when itn generates multiple queries
Calculates measures on result sets whenn necessary
Provides the data to the Siebel
Analytics Web} Server
Siebel Analytics Server Details
Several important componentsn are used by the Siebel Analytics Server
Repository file (.rpd)}
} Cache
NQSConfig.ini}
DBFeatures.ini}
Log files}
Siebel Relationship Management Warehouse
Prebuilt database in star scheman format
Uses Siebel Analytics tools to design,
manage, and run routines ton extract, transform, and load (ETL) data from the Siebel
eBusiness Applications (transactional) database and external databases
Siebel Analytics Administration Tool
Tool to build a metadata modeln
Outputs an repository file that is used by the services
to resolve requests in an optimized fashion.
