SIEBEL ANALYTICS FAQS

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 the areas of your organization’s business

 Have names that correspond to the type of information they contain

 Select columns from 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

 Are pages in a Siebel Analytics application used to display:

 Results of one or more saved Siebel Analytics requests

 Other content items, such as

 Links to Web sites

 ActiveX objects

 HTML text

 Links to documents

 Embedded content: images, text, charts, tables

 Are provided in Siebel Analytics applications

 Can be created by Siebel Analytics users or application developers

 Can be shared by common groups of users

 Can 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 Siebel Answers

Provide rebuilt, fully-interactive access to analytics information

Siebel Analytics Architecture

 Is made up of five main 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 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

Repository File (.rpd)

 Contains metadata that represents the analytical model

 Is created using the Siebel Analytics Administration Tool

 Is divided into three layers

 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 queries

 Is used to eliminate redundant queries to database

 Speeds up results processing

 Query caching is optional

 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

 NQServer.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

Data Sources

 Contain the business data users want to analyze

 Are accessed by the Siebel Analytics Server

 Can be in any format, such as

 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 Siebel application data

 Relevant data structures support Siebel eBusiness Applications 

 Is in a star schema format

 Is included with Siebel Analytics Applications (not available with standalone Analytics purchases)

DAC and Informatica Server

 Data Warehouse Application 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 Server

 Organizes ETL requests for processing

 Third party Informatica Server populates the 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

 Uses Source Dependent Extraction (SDE) routines to extract data

 Loads data into staging tables within the SRMW

 Uses Source Independent Loading (SIL) 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

 Supports the Siebel Analytics Applications

 Parallels the Siebel eBusiness Applications architecture

Implementation

 Siebel Analytics components are often implemented across several computers on the network

 For example:

Clustering Siebel Analytics Servers

 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 the 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 sources

 Organizes 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

 Two major ways to organize data, each optimized for different uses

 Transactional systems 

 Organize data to optimize transactional throughput: inserts, updates, and deletes

 Example: Siebel transactional database

 OLTP

 Transactional schema optimized for read/write—multiple joins

 Analytical systems 

 Organize data to optimize queries on large datasets on separate database instance

 Example: Siebel Relationship Management Warehouse (SRMW) 

 OLAP

 Analytics schema optimized for querying large datasets—few joins

 Star Schema 

 Organizes data into a central fact table with surrounding dimension tables

 Each dimension row has many associated fact rows

 Dimension tables do not directly relate to each other

Sales fact table with dimension tables and relationships 

 Contains business measures or metrics 

 Data is often numerical

 Is the central table in the star

 Contains attributes or characteristics about the business

 Data is often descriptive (alphanumeric)

 Qualifies the fact data

 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 answer “Show me X by Y by Z” type questions

 Example: Show me sales by product by month 

 Siebel Analytics is sold in two varieties

 Siebel Analytics standalone

 Siebel Analytics Applications

 Access Siebel data only (CRM Edition) 

 Access Siebel and/or other data (Enterprise Edition)

Siebel Analytics Standalone

 Provides a platform to model data so users can understand it

 Provides server to generate SQL and seamlessly access and manipulate data from multiple sources

 Provides a simple to use, highly interactive, Web-based analysis tool and the ability to pre-construct dynamic reports and alerts

Siebel Analytics Applications

 Provides all 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 Answers

 Siebel Delivers

 Siebel Analytics Server and Siebel Analytics Web

 Siebel Relationship Management Warehouse (SRMW)

 Siebel Analytics Administration Tool

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 a self-service analysis platform

 Is rendered from information in the Siebel Analytics Server and Siebel Analytics Web Server

Siebel Delivers

 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

 Capabilities and content tailored to the device

 Client application that:

 Is used to create iBots

 Delivers alerts to subscribed users

 Is integrated with Dashboards and Answers

 Job identifies what information to filter, when it should run, and who to send alerts to

Siebel Analytics Server and Siebel Analytics Web Server

 Services that access data and return results to the user

 Determine appropriate source, generate SQL, and merge and sort as necessary

Siebel Analytics Web Server

 Provides the processing to visualize the information for client consumption

 Is implemented as an extension to a Web server

 Uses the web catalog file (.webcat) to store aspects of the application

 Receives data from the Siebel Analytics Server and provides it to the client that requested it

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

 Merges results when it generates multiple queries

 Calculates measures on result sets when necessary

 Provides the data to the Siebel Analytics Web Server

Siebel Analytics Server Details

 Several important components 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 schema format

 Uses Siebel Analytics tools to design, manage, and run routines to 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 model

 Outputs a repository file that is used by the services to resolve requests in an optimized fashion.