# What is the difference between a fact table and a dimension table?

## What is the difference between a fact table and a dimension table?

The fact table contains business facts (or measures), and foreign keys which refer to candidate keys (normally primary keys) in the dimension tables. Contrary to fact tables, dimension tables contain descriptive attributes (or fields) that are typically textual fields (or discrete numbers that behave like text).

## Which are the most dimensional and expressive fact tables?

Atomic transaction grain fact tables
Atomic transaction grain fact tables are the most dimensional and expressive fact tables; this robust dimensionality enables the maximum slicing and dicing of transaction data. Transaction fact tables may be dense or sparse because rows exist only if measurements take place.

Are fact tables normalized or denormalized?

Fact tables are completely normalized To get the textual information about a transaction (each record in the fact table), you have to join the fact table with the dimension table. Some say that fact table is in denormalized structure as it might contain the duplicate foreign keys.

Why do we need fact and dimension table?

A fact table works with dimension tables. A fact table holds the data to be analyzed, and a dimension table stores data about the ways in which the data in the fact table can be analyzed. Thus, the fact table consists of two types of columns.

### What is the best alternative to star schema?

star-snowflake schema.

### Is fact table is Normalised?

Fact tables are completely normalized The fact table contains foreign keys to the dimension tables. To get the textual information about a transaction (each record in the fact table), you have to join the fact table with the dimension table.

Which table will load first fact or dimension?

The process of loading the base schema requires that fact tables and dimension tables be loaded. Fact tables bear foreign keys to the dimension tables and are therefore dependent entities. This would suggest that dimension tables be loaded first.

Is dimensional model denormalized?

Dimensional models are also denormalised in some respects. The ‘john’, ‘jon’ example is also not an example of a snowflake dimension, and neither would you first use a dimensional model and a denormalised table. A dimension is denormalised and has all the detailed you need.

#### Why a factless fact table is used?

Factless fact tables are only used to establish relationships between elements of different dimensions. And are also useful for describing events and coverage, meaning tables contain information that nothing has happened. It often represents many-to-many relationships. The only thing they have is an abbreviated key.

#### Does fact table have primary key?

The fact table also has a primary (composite) key that is a combination of these four foreign keys.

Where does the Overkill command for parcel data come from?

Frequently, parcel data comes from geographic information system (GIS) sources or from existing plats. The quality of this data can vary, making a direct conversion to Civil 3D parcels difficult. An excellent tool for ensuring that the conversion of these lines, arcs, and polylines goes smoothly is the Overkill command.

What’s the tolerance setting for the Overkill command?

The Tolerance setting determines how close two lines can be before they are considered to be duplicates. The higher this value, the more overlap and duplicates the command will find. The check boxes indicate properties Overkill will disregard when performing its edits.

## How to use Overkill to clean up drawings?

The plus signs next to the grips indicate where multiple grips overlap. After you run the Overkill command, the selection shows fewer visible grips and no indication that there are overlapping objects. As you can see, the Overkill command can save you valuable time cleaning up drawings.

## Is it hard to audit data in Kimball?

Because of the cleansing, enriching, etc the lineage of data is very hard in the ‘traditional’ datawarehousing systems like Kimball and Inmon. Auditing is very difficult in systems like Kimball and Inmon. Lindsteds says that you should import the data in the datawarehouse as it is as in the source system.