Siebel analytics Interview Useful Data

Siebel analytics Interview Questions Useful Data
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.

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