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OBIEE is the acronym for Oracle Business Intelligence Enterprise Edition. The texts of OBIEE are 11g, and moreover, the previous version is 10.13. OBIEE also refers to an analytical software engine that gives way to data mining and data analysis. It also comprises publishing capabilities that are on top of the physical data warehouse. In this context, one should note that the base product of OBIEE is also known as Siebel Analytics. It was a result product of the Siebel Corporation. After a considerable time period, it was taken over by the Oracle Corporation.
To carry out this task, you need to click on modify, and then you have to click on sort. It is present in the relevant column in the selection tab.
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You can facilitate the creation of Interactive Dashboards by clicking on the administration and manage dashboards section. In the next step, you have to add a column selector so that the process of the creation of the Interactive Dashboard can be completed.
You can do that by clicking on the modify request tab. Moreover, you have to click on the narrative view section and have to input results so that one can also give a heading for no results by even clicking on the fictional part.
The option of providing a column that is also updatable is also called the write-back option.
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You can achieve Direct SQL by clicking on the Direct Database Request which is located below the area of the subject tab. In this check, you can facilitate Direct SQL in OBIEE.
There are mainly two types of variables in the OBIEE 11g version. They are the Session Variable and Repository Variable. In this context, it is vital to note that the session variable is of two types. They are the non-system variable and the system variable. The illustrations of the non-system variable usually comprise user-defined filters. On the other hand, system variables comprise generally NQ_SESSION.
To carry out these assignments, you can use the merge option in the Admin tool. Moreover, in the case of reports and dashboards, one can also use the framework of a content accelerator.
If you want to carry out this function, you have to make sure that the table exists or not. If the table exists, then you have to click on BMM and later on the cover of the presentation. One can also reload the metadata server so that it would be visible to the interested viewers.
Table alias can be used for facilitating self-joins. Moreover, you can create a table alias by right-clicking on the table in the natural cover, and then you can click on a nickname.
To carry out the enabling function, you can use ENABLE function under the CACHE Section for the System Level Administrator. On the other hand, in case you want to enable cache then you have to make sure that you open the repository tab in the offline mode. It would be different from the current repository, and then you can opt to click on enable or disable functions to facilitate cache.
Level-based metrics usually denote to hold a certain level of the dimension. For instance, quarterly sales or monthly sales can be dragged into the category of standard-based metrics. In order to facilitate the creation of level-based parameters, you have to generate a measure of concentration found along with a new logical column. However, make sure that you create that to an appropriate level in the hierarchy of the dimension.
Data warehouse refers to the primary storehouse of a company's historical data. Hence, it is also referred to as corporate memory. It comprises the raw material for the decision support of the management. The use of a data warehouse can be determined by the fact that a data analyst can carry out complex queries and assessments. The collection of data in data warehousing refers to the supported form of decision making which is helpful to analyze the various forms of big data critically. It comprises a plethora of varieties of data that always depict a consistent image of the business conditions.
The Query repository tool still facilitates the choice of analyzing and searching data from the collection of a database as per the type, name, and other types of attributes. There exists a relationship between the various view layers of data that corresponds to the thickness of concrete columns.
It is important to note that opaque views denote tables that are manufactured with query data. It comprises the select query option. Moreover, it is also interesting to mention that the misty pictures make use of logical comprehension for the simple implementation of data. However, there might be constraints related to big performance. They are only utilized when there exists no visible way to calculate the final solution.
The logical table that is created at the layer of BMM can be emphasized on the data output which is being generated from a physical stand. In this situation, it is also called a single Logical Table source. When a dependency situation arises on the columns of vivid physical layer tables, it is known by the name of Multiple LTS. As an OBIEE administrator, one would be dealing with Multiple LTS.
OBIEE architecture functions on the basis of a request that is made by the users which are being sent to the presentation server. In the subsequent process, the presentation server always transforms the application into logical SQL which is being sent to the Business Intelligence Server. In the next operation, the BI server alters the valid SQL into the physical SQL and then is being submitted to the database. In the last process, the results that are generated always get back to the same path.
These comprise different types of elements that are mainly categorized mainly into two categories. They are Client Components and Server Components. In this context, it is vital to note that the client component is further classified into non-web-based clients and web-based clients.
There are different kinds of security providers that are being used by OBIEE. For instance, authentication facilitates authentic users. The provider of the policy store is being utilized to access various types of privileges on various kinds of applications except for the BI presentation services. The credential store provider is being used to store different kinds of credentials that are internally being used by the applications related to the Business Intelligence application.
Repo variables are also known by the name of repository variables. It comprises a minute exponent at a particular time. The repo variables can be utilized instead of constraints or literals that can arise while executing various functions. The Oracle BI server substitutes the notion of the repo variable in the existence of the metadata. On the other hand, session variables are similar to the dynamic repo variables. They obtain these values from the initialization blocks. However, the initialization of the session variables is not scheduled correctly. There are many instances of a session variable as it comprises active sessions.
Managing and monitoring data and cache in the environment of OBIEE is called management of cache. There exist three methods to carry out the notion of management of cache. For instance, persistence time for cache physical tables, setting polling tables related to events, and disabling cache are the strategies.
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One can assign a unique quality for physical tables of various natures. For instance, if the queries involve the specified table, they can be added to the cache to answer future questions. In order to enable cache for a unique type of physical table, you can assign the table layer of the tool which lies in the administration layer. You can choose the option to make table choice properties which are located on the general tab. One can also utilize data for the creation of a unique table. This is also useful for all kinds of data sources of OLTP.
On the other hand, disabling caching for the system by setting the enable parameter would also allow you to make sure that you have created the INI configuration file. Disable of various types of caches also stops the entrance of new queries by using the existing stockpile. The disabling of cache would also allow you to enable it at a later time without losing any original entries that are already stored in the cache.
The polling table of events is an exclusive component of the Siebel Analytics Server. It is a polling table that usually stores data about the current updates in various databases. Moreover, an application can also be configured to add rows so that a polling table of events each time can be produced. The server of the analytics in a Siebel environment can be set at particular intervals. It also eliminates the occurrence of any cache during this process. Moreover, a polling table of events is mainly seen as a unique table and does not need to be intersected with the other tables that are present on the natural cover.
It is an arrangement method that is utilized to produce various reports.
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Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.