A factless fact table is a fact table that does not have any measures. It is essentially an intersection of dimensions. On the surface, a factless fact table does not make sense, since a fact table is, after all, about facts. However, there are situations where having this kind of relationship makes sense in data warehousing.
What is a fact-less fact in data warehouse?
The fact-less fact is often used to resolve a many-to-many cardinality issue. Types of Fact-less fact tables in Data Warehouse? This type of fact table establishes the relationship among the various dimension members from various dimension tables without any measured value.
What is fact table in data warehouse?
A Fact table is typically a table created in a data warehouse which contains facts such as total number of employees in an organization or average sales figures for all the products and so on. How a dimension is related to a fact table?
When to use factless fact table in sales?
Factless fact table can be also used in these situations: Tracking salesperson assigned to each prospect or customer. Logging the eligibility of employees for a compensation program. Capturing the promotion campaigns that are active at specific times such as holidays.
Which is an example of a fact-less fact?
For examples, Student attendance (student-teacher relation table) capturing table is the fact-less fact. Table will have entry into it whenever student attend class. Following questions can be answered by the student attendance table: Which student is taught by the maximum number of teachers? Which class has maximum number of attendance?
What is Factless table in data warehouse?
Factless tables simply mean the key available in the fact that no remedies are available. 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.
What is factless fact table give example?
A factless fact table is a fact table that does not have any measures, i.e. any numeric fields that can be aggregated. For example, if you are modelling product sales, you can have a Sales fact table that will contain the dimension keys and, for example, the "amount" value/measure, to record the amount sold.
How many attributes does factless fact table have?
There are two kinds of factless fact tables: Factless fact table describes events or activities. Factless fact table describes a condition, eligibility, or coverage.
What is called junk dimension?
A Junk Dimension is a dimension table consisting of attributes that do not belong in the fact table or in any of the existing dimension tables. The nature of these attributes is usually text or various flags, e.g. non-generic comments or just simple yes/no or true/false indicators.
What is fact table and Factless table?
A factless fact table is a fact table that does not have any measures. It is essentially an intersection of dimensions (it contains nothing but dimensional keys). There are two types of factless tables: One is for capturing an event, and one is for describing conditions.
What are the SCD types?
What are the types of SCD?Type 0 – Fixed Dimension. No changes allowed, dimension never changes.Type 1 – No History. Update record directly, there is no record of historical values, only current state.Type 2 – Row Versioning. ... Type 3 – Previous Value column. ... Type 4 – History Table. ... Type 6 – Hybrid SCD.
What are the 3 types of fact tables?
These are:Transaction fact tables.Periodic snapshot tables, and.Accumulating snapshot tables.
What are the three types of fact tables?
There are three types of fact tables and entities: Transaction. A transaction fact table or transaction fact entity records one row per transaction. Periodic.
Is star schema normalized or denormalized?
Star schema's dimension tables do not contain any foreign keys. That is, the dimension tables do not reference any other tables, nor do they have any "sub-dimension tables." They are generally denormalized because some information may be duplicated in the dimension tables.
What are the 3 types of dimensions?
Based on the frequency of change of dimension it can be classified into three types:Static Dimension: Dimensions which does not change over time. ... Slowly changing dimension(SCD): Dimensions that change or can change slowly over time. ... Rapidly Changing Dimension: Dimensions that change or can change rapidly over time.
Why do we use junk dimension?
A junk dimension combines several low-cardinality flags and attributes into a single dimension table rather than modeling them as separate dimensions. There are good reasons to create this combined dimension, including reducing the size of the fact table and making the dimensional model easier to work with.
What is star and snowflake schema?
Star and snowflake schema designs are mechanisms to separate facts and dimensions into separate tables. Snowflake schemas further separate the different levels of a hierarchy into separate tables. In either schema design, each table is related to another table with a primary key/foreign key relationship.
About Factless Fact Table
This tutorial assumes that the reader is familiar with SQL Server database and data warehouse business intelligence concepts. As well as is comfortable with the common terms used and applied in this field (of business intelligence).
When to use Factless FACT Tables
In order to understand the use of Factless FACT table we have to refer to, Kimball Group, one of the earliest pioneers in the field of Data Warehouse. According to Kimball Group, "It is possible that the event merely records a set of dimensional entities coming together at a moment in time.
How to use FactLess FACT Table
Let us now look at a common example of how to use a Factless Fact table.
Factless fact table for event or activity
When designing a dimensional model, you often find that you want to track events or activities that occur in your business process but you can’t find measures to track. In these situations, you can create a transaction-grained fact table that has no facts to describe that events or activities.
Factless fact table for event or activity example
For example, you may want to track employee leaves. How often and why your employee leaves are very important for you to plan your daily activities and resources.
What is a factless fact table?
Factless Fact Table. A factless fact table is a fact table that does not have any measures. It is essentially an intersection of dimensions. On the surface, a factless fact table does not make sense, since a fact table is, after all, about facts. However, there are situations where having this kind of relationship makes sense in data warehousing.
Why is adding a fact that always shows 1 redundant?
However, adding a fact that always shows 1 is redundant because we can simply use the COUNT function in SQL to answer the same questions.
What is the purpose of Factless Fact Table?
A tracking process or collecting status can be performed by using fact less fact tables. The fact table does not have numeric values that are aggregate, hence the name. Mere key values that are referenced by the dimensions, from which the status is collected, are available in fact less fact tables.
What is the difference between a materialized view and a view?
What is the difference between view and materialized view? - A view is created by combining data from different tables. Hence, a view does not have data of itself....Materialized view usually used in data warehousing has data........
Is a snow flake schema more complex than a star schema?
Data warehousing star & snowflake - A snow flake schema design is usually more complex than a star schema. In a star schema a fact table is surrounded......
What is a factless fact table?
A Data Warehouse fact-less fact table is a fact that does not have any measures stored in it. This table will only contain keys from different dimension tables. The fact-less fact is often used to resolve a many-to-many cardinality issue.
Can a factless fact table answer a positive query?
A fact-less-fact table can only answer ‘optimistic’ queries (positive query) but cannot answer a negative query. Coverage fact is used to support negative analysis reports. For example, an electronic store did not sell any product for give period of time.