Attributes in Logical Data Models

Attributes describe the entity, a fact, with which they are associated. In other words, rather than stating a fact, they describe it - just like columns in a table. In reports, they are used to group, slice, filter, and reorder facts.

Contents:

Attributes in a dataset are identified by the following icon:

The following image shows an LDM Modeler dataset comprising four attributes:

Attributes are typically stored in an integer or text format:

ComponentData TypeNotes
AttributeTextUp to 10,000 characters
Integer (INT)

Integer values in the range of min(-2147483648) to max(2147483647)

By identifying attributes as numeric data, the GoodData Portal properly sorts them in reports.

Large Integer (BIGINT)

Integer values in the range of min(-1e+15) to max(1e+15)

By identifying attributes as numeric data, the GoodData Portal properly sorts them in reports.

Dimensions

A set of related attributes is called a dimension. For example, Address, City, State, and Zip Code may be related in a dimension called Location. Each attribute in the dimension is a discrete entity, yet they are all related to each other.

A dimension is stored in a dimension table. Dimension tables are typically wide and shallow (they do not have many rows).

Your dimensions should always have consistent definitions and contents. Dimensions that share identical structures are called conformed dimensions. Conformed dimensions are easier to create insightful reporting because of consistency between the data.

For example, the State attribute should not use two-letter abbreviations (CA) along with full state names (California). Queries using this malformed attribute will not be able to match the two versions of the state name.

Whenever possible, share dimensions between fact tables to ensure consistency. Shared dimensions are always conformed.

Attribute Values and Labels

An attribute can have multiple values which you can further define by the use of labels. Combining related attributes creates a dimension.

The following table shows the relationship of values and attributes in your logical data model.

AttributeAttribute LabelAttribute Values

Department

Full names

Human Resources

Research and Development

Quality Assurance

Shortened names

HR

RD

QA

Numbers

1

2

3

Attribute Labels

A label is an object that you use to apply additional descriptors to an attribute. Every attribute has a default label.

For example, the Person attribute may have the following labels: FirstName, LastName, SocialSecurity#, and others. When you define an attribute in LDM Modeler, a default label is automatically created for you.

Default label is used for the data load process. If you want to change it, ensure that you make corresponding changes in your source data.

Attribute Values

In addition to the examples listed in the table, in the attribute Name, values can be John Dow, Mary Jane and so on. Facebook and Twitter are values of the Sales Channel attribute. Attribute values can also be numeric - in an attribute Table Size, values can be 1, 2, 4, and so on.

Numerical data can be both facts and attributes. For example, you can track Age both as a fact and as an attribute to:

  • enable segmentation
  • use Age in computation - attributes cannot be used in computations

Recommended Practices

  • Avoid placing attributes in fact tables. Fact tables should contain facts and foreign keys to attributes stored in other dimensions.
  • Create common dimensions that can be reused (shared) when you create additional fact tables. For example, you should have only one dimension table for customer, one for product, one for an employee, and so on. These conformed dimensions ensure uniformity of data in the project and enable re-use of the associated contextual information.
    Name your model objects in a consistent manner that is understandable by business users, since they will interact with them in project that uses your model.
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