Host
A host can be a physical computer or virtual machine.
Server
A server is an Operating System (OS) level process hosting one or more services.
For example,
CMS and Adaptive Processing Server are servers.
A server runs under a specific OS account and has its own PID.
Service
A service is a server subsystem that provides a specific function. The service runs within the
memory space of its server under the Process ID (PID) of the parent container (server).
For example,
the Web Intelligence Scheduling and Publishing Service is a subsystem running within the Adaptive Job Server.
Node
A node is a collection of BusinessObjects Enterprise servers, all running on the same host.
One or more nodes can be on a single host. Each node is managed by a Server Intelligence Agent (SIA).
Server Intelligence Agent
The SIA is a locally run service managed by the operating system. The task of the Server
Intelligence Agent (SIA) is to start, stop, and monitor locally run BusinessObjects servers.
When one of the managed servers goes down unexpectedly, the SIA restarts the server immediately.
When you issue a command in the CMC to stop a server, the SIA stops the server.
When you create a SIA, you create a new node. A node is a collection of BusinessObjects
Enterprise servers which run on the same host and are managed by a single SIA.
You can add servers to the node and you can have more than one node on the same machine.
The SIA continuously monitors server status information, which is stored in the CMS databases.
When you change a server's settings or add a new server in the CMC, the CMS notifies the SIA
and the SIA performs the jobs accordingly.
................ continue..next we will discuss about Workflows for each servers and how the communicate each server
Tuesday, September 21, 2010
Tuesday, September 14, 2010
Introduction to Semantic Layers
Introduction to Semantic Layers
The semantic layer is a metadata layer that abstracts the complexities of the data source. The end user sees only a logical grouping of available, well-named fields for use and does not have to concern them with the details of database design or need any SQL knowledge.
In Business Objects XI, two semantic layers are supported business views and universes. With the merging of Crystal Decisions and Business Objects, a decision was made to keep business views from Crystal Decisions and universes from Business Objects.
Why Use a Semantic Layer
Semantic layers offer wonderful advantages over traditional report design processes by removing the most difficult data-intensive tasks, as described in the following sections, from report design. Furthermore, it allows for reuse of components and promotes the concept of changing a component once and having the change applied to multiple reports.
Abstract the Complexities of the Database
With large complex data warehousing projects using many tables and complex joins, the report author might not have the requisite knowledge. If the report author is required to join the tables for the reports, he might not use the most efficient join, resulting in poor performance. Semantic layers allow for a division in labor, whereby the more technical database administrators and developers can create the joins between tables and data sources and the business users can concentrate on designing reports to satisfy their requirements.
Putting Report Design in the Hands of Business People
As a result of this division of labor and the reuse of the Business Objects repository, business people do not have to be as technically savvy, effectively resulting in less technical skill being required to develop reports.
Support for a Wide Range of Data Sources
Both metadata layers provide a wide range of database support, including the majority of relational database vendors. Business views also include the ability to connect to some no relational data sources; for example, a user could abstract the complexity of an XML file by encapsulating the XML with a business view.
Reuse of Metadata Components
One major drawback to specifying the data joins in each report is that this creates a large amount of redundant work. Secondly, should the database change, there is no way to effect this change other than changing each of the individual reports. A semantic layer allows the administrator to create this join once and should this join change, the administrator only needs to change it once.
Security
The semantic layer allows the administrator to set up security so that data is filtered based on who the user is. This can be based off of security within an entitlements database, or the Business Objects user model can be used to create the security model. This way rows and columns can be secured by users or groups.
The Business Objects repository also secures the semantic layers and only those users with the required permissions can access them. For example, it is generally accepted auditing practice that accounts receivable personnel should not be able to see accounts payable information and vice versa. By using the user group functionality in Business Objects Enterprise, the accounts receivable group would be given access to only the accounts receivable semantic layer.
Transportability
The semantic layers provide the ability to export a business view to an XML file or a universe to an .unv file. These files can then be easily imported into another Business Objects Enterprise repository. This simple form of transportability makes semantic layer swapping a simple exercise. It also simplifies the storage of source code, should the organization have a source code storage requirement.
The semantic layer is a metadata layer that abstracts the complexities of the data source. The end user sees only a logical grouping of available, well-named fields for use and does not have to concern them with the details of database design or need any SQL knowledge.
In Business Objects XI, two semantic layers are supported business views and universes. With the merging of Crystal Decisions and Business Objects, a decision was made to keep business views from Crystal Decisions and universes from Business Objects.
Why Use a Semantic Layer
Semantic layers offer wonderful advantages over traditional report design processes by removing the most difficult data-intensive tasks, as described in the following sections, from report design. Furthermore, it allows for reuse of components and promotes the concept of changing a component once and having the change applied to multiple reports.
Abstract the Complexities of the Database
With large complex data warehousing projects using many tables and complex joins, the report author might not have the requisite knowledge. If the report author is required to join the tables for the reports, he might not use the most efficient join, resulting in poor performance. Semantic layers allow for a division in labor, whereby the more technical database administrators and developers can create the joins between tables and data sources and the business users can concentrate on designing reports to satisfy their requirements.
Putting Report Design in the Hands of Business People
As a result of this division of labor and the reuse of the Business Objects repository, business people do not have to be as technically savvy, effectively resulting in less technical skill being required to develop reports.
Support for a Wide Range of Data Sources
Both metadata layers provide a wide range of database support, including the majority of relational database vendors. Business views also include the ability to connect to some no relational data sources; for example, a user could abstract the complexity of an XML file by encapsulating the XML with a business view.
Reuse of Metadata Components
One major drawback to specifying the data joins in each report is that this creates a large amount of redundant work. Secondly, should the database change, there is no way to effect this change other than changing each of the individual reports. A semantic layer allows the administrator to create this join once and should this join change, the administrator only needs to change it once.
Security
The semantic layer allows the administrator to set up security so that data is filtered based on who the user is. This can be based off of security within an entitlements database, or the Business Objects user model can be used to create the security model. This way rows and columns can be secured by users or groups.
The Business Objects repository also secures the semantic layers and only those users with the required permissions can access them. For example, it is generally accepted auditing practice that accounts receivable personnel should not be able to see accounts payable information and vice versa. By using the user group functionality in Business Objects Enterprise, the accounts receivable group would be given access to only the accounts receivable semantic layer.
Transportability
The semantic layers provide the ability to export a business view to an XML file or a universe to an .unv file. These files can then be easily imported into another Business Objects Enterprise repository. This simple form of transportability makes semantic layer swapping a simple exercise. It also simplifies the storage of source code, should the organization have a source code storage requirement.
Wednesday, September 8, 2010
Inmon vs. Kimball - An Analysis
Mr. William (Bill) Inmon is known as the “Father of Data Warehousing”, entitled for coining the term “Data Warehouse” in 1991. He defined a model to support “single version of the truth” and championed the concept for more than a decade. He also created “Corporate Information Factory” in collaboration with Ms. Claudia Imhoff. Mr. Inmon is known to have published 40+ books and 600+ articles.
Mr. Ralph Kimball is known as the “Father of Business Intelligence” for defining the concept behind “Data Marts”, for developing the science behind the analytical tools that utilize dimensional hierarchies, and for conceptualizing star-schemas and snowflake data structures. He defined a model to support analytical analysis and championed data marts for more than a decade. Though Kimball’s writings do not exceed Inmon’s by quantity, Kimball’s books are all-time best sellers on data warehousing.
Inmon and Kimball are two pioneers that started different philosophies for enterprise-wide information gathering, information management, and analytics for decision support. Inmon believes in creating a single enterprise-wide data warehouse for achieving an overall business intelligence system. Kimball believes in creating several smaller data marts for achieving department-level analysis and reporting.
APPROACHES
Inmon’s philosophy recommends to start with building a large centralized enterprise-wide data warehouse, followed by several satellite databases to serve the analytical needs of departments (later known as “data marts”). Hence, his approach has received the “Top Down” title.
Kimball’s philosophy recommends to start with building several data marts that serve the analytical needs of departments, followed by “virtually” integrating these data marts for consistency through an Information Bus. Hence, his approach received the “Bottom Up” title. Mr. Kimball believes in various data marts that store information in dimensional models to quickly address the needs of various departments and various areas of the enterprise data.
STRUCTURES
Besides the differences in approaches, Inmon and Kimball also differ in the structure of the data. Inmon believes in creating a relational-model (third normal form: 3NF) where as Kimball believes in creating a multi-dimension model (star-schema and snowflakes).
Inmon argues that once the data is in a relational model, it will attain the enterprise-wide consistency which makes it easier to spawn-off the data-marts in dimensional-models. Kimball argues that the actual users can understand, analyze, aggregate, and explore data-inconsistencies in an easier manner if the data is structured in a dimensional-model. Additionally, to enable the Information Bus, data marts are categorized [Imhoff, Mastering Data warehouse design] as atomic data marts, and aggregated data marts that both use dimensional-models.
Irrespective of the structural differences in the model, both Inmon and Kimball agrees that there is a need to separate the detailed-level data from aggregated-level data.
CONTENT
Another difference is in the granularity of the content. Inmon believes that the content in the data warehouse has to be at the most granular level possible and must include all the possible historical data within an enterprise. His argument is that the end-users will mandate the needs on the level of data-detail that are not known at the time of building the data warehouse.
COMMON GOALS
Though Mr. Inmon and Mr. Kimball have different philosophies to their approach, they do tend to agree with each other in an indirect manner. Though Inmon’s basis is on a single data warehouse, he stressed on iterative approach and discouraged the “big bang” approach. On the other hand, though Kimball’s philosophy is to quickly create few successful data marts at a time, he stresses on integration for consistency via an Information Bus.
DATA WAREHOUSE vs. BUSINESS INTELLIGENCE
Business Intelligence = Inmon’s Corporate Data Warehouse + Kimball’s Data Marts + Data Mining + Unstructured Data.
Mr. Ralph Kimball is known as the “Father of Business Intelligence” for defining the concept behind “Data Marts”, for developing the science behind the analytical tools that utilize dimensional hierarchies, and for conceptualizing star-schemas and snowflake data structures. He defined a model to support analytical analysis and championed data marts for more than a decade. Though Kimball’s writings do not exceed Inmon’s by quantity, Kimball’s books are all-time best sellers on data warehousing.
Inmon and Kimball are two pioneers that started different philosophies for enterprise-wide information gathering, information management, and analytics for decision support. Inmon believes in creating a single enterprise-wide data warehouse for achieving an overall business intelligence system. Kimball believes in creating several smaller data marts for achieving department-level analysis and reporting.
APPROACHES
Inmon’s philosophy recommends to start with building a large centralized enterprise-wide data warehouse, followed by several satellite databases to serve the analytical needs of departments (later known as “data marts”). Hence, his approach has received the “Top Down” title.
Kimball’s philosophy recommends to start with building several data marts that serve the analytical needs of departments, followed by “virtually” integrating these data marts for consistency through an Information Bus. Hence, his approach received the “Bottom Up” title. Mr. Kimball believes in various data marts that store information in dimensional models to quickly address the needs of various departments and various areas of the enterprise data.
STRUCTURES
Besides the differences in approaches, Inmon and Kimball also differ in the structure of the data. Inmon believes in creating a relational-model (third normal form: 3NF) where as Kimball believes in creating a multi-dimension model (star-schema and snowflakes).
Inmon argues that once the data is in a relational model, it will attain the enterprise-wide consistency which makes it easier to spawn-off the data-marts in dimensional-models. Kimball argues that the actual users can understand, analyze, aggregate, and explore data-inconsistencies in an easier manner if the data is structured in a dimensional-model. Additionally, to enable the Information Bus, data marts are categorized [Imhoff, Mastering Data warehouse design] as atomic data marts, and aggregated data marts that both use dimensional-models.
Irrespective of the structural differences in the model, both Inmon and Kimball agrees that there is a need to separate the detailed-level data from aggregated-level data.
CONTENT
Another difference is in the granularity of the content. Inmon believes that the content in the data warehouse has to be at the most granular level possible and must include all the possible historical data within an enterprise. His argument is that the end-users will mandate the needs on the level of data-detail that are not known at the time of building the data warehouse.
COMMON GOALS
Though Mr. Inmon and Mr. Kimball have different philosophies to their approach, they do tend to agree with each other in an indirect manner. Though Inmon’s basis is on a single data warehouse, he stressed on iterative approach and discouraged the “big bang” approach. On the other hand, though Kimball’s philosophy is to quickly create few successful data marts at a time, he stresses on integration for consistency via an Information Bus.
DATA WAREHOUSE vs. BUSINESS INTELLIGENCE
Business Intelligence = Inmon’s Corporate Data Warehouse + Kimball’s Data Marts + Data Mining + Unstructured Data.
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