Framework Manager Best Practices Pdf

It can easily generate facts with correct aggregations with this simple scenario. The usage of a field can be an identifier, attribute or fact. How easily can the model be adapted to changing conditions and how easily can the user generate ad hoc query requests? Leave a Reply Cancel reply Your email address will not be published. Therefore it may not adequately identify issues in every environment.

The dimensional layer is required only for models which include dimensionally modeled data. These situations can be manxger by creating multiple model query subjects for every occurrence. Row level security can be put in place in two different ways. The first occurs when there are multiple valid relationships. It is essential to ensure cardinality is set correctly throughout your model to ensure unnecessary stitch queries which can cause report performance to be very poor.

Relationships and Cardinality

Also realize that stitch queries are sometimes best way of issuing a query. What are your best-practices? Understanding the implications of cardinality in relationships is the best defense against this. Many of the best practices can be thought of in terms of what development activities should or should not be performed in each of the layers. When a table has separate usages, or meanings, harry potter 7 pdf french it should be aliased.

Cognos Expert Cognos Framework Manager Best Practices

When there are multiple columns for every language, the modeler can specify that the column name retrieved at runtime is dependent of the user language. Creating joins manually gives full control and avoids cardinality and join issues. These scope relations are only logical, the underlying query subject joins remain in use. Applying security at a high level and then drilling down to more detailed security levels will save maintenance and troubleshooting time.

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Cognos framework manager best practices

This will prevent users from developing run-away queries that can impact database performance. Another area of confusion is naming conventions and nomenclature, and areas surrounding these subjects. For every query item, the modeler should check if the usage is set correctly.

Packages, data source connections and parameter maps fall outside of the modelling layers. Level information is used to roll up the measures. The data layer, also called the import layer, contains the data source query subjects, based directly on the underlying database objects. This can be handled by using the Metadata Wizard.

The model query subject will logically condense the snowflake into one object, thus enforcing the correct context in every query. Arrange query subject items in a user-friendly manner.

We have also created some special query subjects here that apply sequence numbers to our date table. Doing so will enable you to see what database columns are in a report when debugging the report.

Relationships and Cardinality

Namespaces will structure frameworks. The final step in modeling a framework is creating a Presentation Layer. Part of the aggregate lacuna in Framework Manager can be filled by using Dynamic Cubes. Use data level security settings in Framework Manager only when absolutely necessary. Cognos Framework Manager Best Practices The first occurs when there are multiple valid relationships.

While debugging reports, it can be quite handy to be able to include the primary key of a table to identify exactly which record has issues. In some circumstances, this may be worth the trade-off, but should be avoided when possible.

In a query where i nventory fact and sales fact are compared at a product dimension level, a stitch query is the correct approach. Most often figures in a cube are summarized so the lowest grain is not available. Please enter your full name. There are a number of drawbacks to do reporting on a relational model. Generally, the layers build upon one another, with the data layer being the foundation of the model.

Renaming of columns is done here. Determinants will change the default behaviour of the query. When you have a sales fact at day level and a target fact at month level, combining both facts in a single query would lead to incorrect results. Relationships and Cardinality The area which causes much confusion and difficulties with modeling is relationships and cardinality. We are lucky enough to be working with a datawarehouse, which allows us to create clear star schemas.

This is also the place where the different roles of dimensions are defined. Each level should have the key and caption defined. Each level is specified identifying the key and attributes that belong to a level.

Get it right the first time. All objects available in the database can be easily queried at the lowest grain.


This is a smooth progression from dimensions to facts. However be aware that namespace names must be unique, and that items within a namespace must likewise be unique. Segments are a very interesting feature to minimize maintenance.