Table of Contents
- 1 What is the grain of a data warehouse?
- 2 What is the grain of a data table?
- 3 What may happen if the grain of the fact table was not designed properly?
- 4 What is grain in dimensional modeling?
- 5 Why aggregate is used in dimensional model of data warehouse?
- 6 Why is data granularity important?
- 7 Why should we separate dimensions and facts instead of combining in one table?
- 8 What is the purpose of aggregation?
- 9 Why declare the grain of a design?
- 10 What is the grain of a fact and dimension table?
- 11 How do you determine the grain of data?
What is the grain of a data warehouse?
In the world of data warehousing, the grain of a fact table defines the level of detail that is stored, and which dimensions are included make up this grain. Obviously, the higher the grain the better- although source systems and data volume/performance may intervene.
What is the grain of a data table?
What Is Data Grain? In data warehousing, granular data or the data grain in a fact table helps define the level of measurement of the data stored. It also determines which dimensions will be included to make up the grain. These measurements of fact describe what you have populated in each row.
What does grain of the data mean row or column?
The GRAIN or GRANULARITY of the fact table refers to the level of detail of each row in the fact table. For example, an order fact table might have a grain of order, with one row per order, or order line, with a row for every line on each order (meaning more than one line for some orders).
What may happen if the grain of the fact table was not designed properly?
If the grain isn’t clearly defined, the whole design rests on quicksand. Discussions about candidate dimensions go around in circles, and rogue facts that introduce application errors sneak into the design. Declaring the grain means saying exactly what a fact table record represents.
What is grain in dimensional modeling?
The grain of the dimensional model is the finest level of detail that is implied when the fact and dimension tables are joined. For example, the granularity of a dimensional model that consists of the dimensions Date, Store, and Product is product sold in store by day.
What is grain of fact?
The grain of a fact table represents the most atomic level by which the facts may be defined. The grain of a sales fact table might be stated as “sales volume by day by product by store”. Each record in this fact table is therefore uniquely defined by a day, product and store.
Why aggregate is used in dimensional model of data warehouse?
Aggregates are used in dimensional models of the data warehouse to produce dramatic positive effects on the time it takes to query large sets of data. A more common use of aggregates is to take a dimension and change the granularity of this dimension.
Why is data granularity important?
Data granularity is the level of detail considered in a model or decision making process or represented in an analysis report. Increased granularity can help you drill down on the details of each marketing channel and assess its efficacy, efficiency, and overall ROI.
Why defining the grain of a fact table is important?
Fact tables provide the (usually) additive values that act as independent variables by which dimensional attributes are analyzed. Fact tables are often defined by their grain. The grain of a fact table represents the most atomic level by which the facts may be defined.
Why should we separate dimensions and facts instead of combining in one table?
Why should we separate dimension and facts instead of combining both in one table. need some insight in dimensional modeling or star schema. Having dimensions in fact table will makes query run very fast and no need to maintain dimension table separately, no need to look up dimension table when doing ETL.
What is the purpose of aggregation?
Data aggregation is often used to provide statistical analysis for groups of people and to create useful summary data for business analysis. Aggregation is often done on a large scale, through software tools known as data aggregators.
Can dimension be aggregated?
You can aggregate a dimension in the view as Minimum, Maximum, Count, or Count (Distinct). When you aggregate a dimension, you create a new temporary measure column, so the dimension actually takes on the characteristics of a measure.
Why declare the grain of a design?
The grain declaration becomes a binding contract on the design. The grain must be declared before choosing dimensions or facts because every candidate dimension or fact must be consistent with the grain. This consistency enforces a uniformity on all dimensional designs that is critical to BI application performance and ease of use.
What is the grain of a fact and dimension table?
Each fact and dimension table has its own grain or granularity. Each table (either fact or dimension) contains some level of detail that is associated with it. The grain of the dimensional model is the finest level of detail that is implied when the fact and dimension tables are joined.
What is atomic grain and why is it important?
This consistency enforces a uniformity on all dimensional designs that is critical to BI application performance and ease of use. Atomic grain refers to the lowest level at which data is captured by a given business process.
How do you determine the grain of data?
The grain is exclusively determined by the physical realities of the source of the data. All grain definitions should start at the lowest, most atomic grain and should describe the physical process that collects the data.