In denodo associations follow the same concept as modeling
tools, which can be described as an ‘on-demand join.’
Should Associations Be Created In the Denodo Model?
You don’t necessarily need to define an Association at every
level; usually, the best practice is to apply associations at the following
On final views published for data consumers,
indicating relationships between related views; Especially, on published web
On the component views below, any derived view
that brings together disparate (dissimilar) data sources. The associations should be defined as
Referential Constraints whenever appropriate to aid the optimization engine.
On the component views below, any derived view
that joins a “Base View from Query” with standard views, since Base
Views from Query cannot be rewritten by the denodo optimization engine. Often Base Views from Query create
These best practices should cover the majority scenarios;
beyond these guidelines, it is best to take an ad-hoc approach to create
Associations when you see a specific performance/optimization.
Are Associations important in Denodo?
In a nutshell, associations performance and the efficiency
of the denodo execution optimizer along with other model metadata, such as:
A coworker recently asked a question as to whether denodo
generated joins automatically from source RDBMS database schema. After searching, a few snippets of
information became obvious. First, that
the subject of inheriting join properties was broader than joins and needed to
in modeling associations (joins on demand). Second, that there were some denodo
design best practices to be considered to optimize associations.
Denodo Automatically Generate Joins From the Source System?
After some research, the short answer is no.
Denodo Inherit Accusations From A Logical Model?
The short answer is yes.
Denodo bridges allow models to be passed to and from other
modeling tools, it is possible to have the association build automatically,
using the top-down approach design approach and importing a model, at the
Interface View level, which is the topmost level of the top-down design
However, below the Interface view level, associations and or joins are created manually by the developer.
A semantic data model is a method of organizing and representing corporate data that reflects the meaning and relationships among data items. This method of organizing data helps end users access data autonomously using familiar business terms such as revenue, product, or customer via the BI (business intelligence) and other analytics tools. The use of a semantic model offers a consolidated, unified view of data across the business allowing end-users to obtain valuable insights quickly from large, complex, and diverse data sets.
What is the purpose
of semantic data modeling in BI and data virtualization?
A semantic data model sits between a reporting tool and the original database in order to assist end-users with reporting. It is the main entry point for accessing data for most organizations when they are running ad hoc queries or creating reports and dashboards. It facilitates reporting and improvements in various areas, such as:
No relationships or joins for end-users to worry about because they’ve already been handled in the semantic data model
Data such as invoice data, salesforce data, and inventory data have all been pre-integrated for end-users to consume.
Columns have been renamed into user-friendly names such as Invoice Amount as opposed to INVAMT.
The model includes powerful time-oriented calculations such as Percentage in sales since last quarter, sales year-to-date, and sales increase year over year.
Business logic and calculations are centralized in the semantic data model in order to reduce the risk of incorrect recalculations.
Data security can be incorporated. This might include exposing certain measurements to only authorized end-users and/or standard row-level security.
A well-designed semantic data model with agile tooling allows end-users to learn and understand how altering their queries results in different outcomes. It also gives them independence from IT while having confidence that their results are correct.
The ‘Comment on Column’ provides the same self-documentation capability as ‘Comment On table’, but drives the capability to the column field level. This provides an opportunity to describe the purpose, business meaning, and/or source of a field to other developers and users. The comment code is part of the DDL and can be migrated with the table structure DDL. The statement can be run independently or working with Aginity for PureData System for Analytics, they can be run as a group, with the table DDL, using the ‘Execute as a Single Batch (Ctrl+F5) command.
Basic ‘COMMENT ON field’ Syntax
The basic syntax to add a comment to a column is:
COMMENT ON COLUMN <<Schema.TableName.ColumnName>> IS ‘<<Descriptive Comment>>’;
Example ‘COMMENT ON Field’ Syntax
This is example syntax, which would need to be changed and applied to each column field:
COMMENT ON COLUMN time_dim.time_srky IS ‘time_srky is the primary key and is a surrogate key derived from the date business/natural key’;