Full Answer
What is a fact table Kimball?
Fact tables are the foundation of the data warehouse. They contain the fundamental measurements of the enterprise, and they are the ultimate target of most data warehouse queries.
What are the 3 types of fact tables?
These are:Transaction fact tables.Periodic snapshot tables, and.Accumulating snapshot tables.
What should be included in a fact table?
In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is located at the center of a star schema or a snowflake schema surrounded by dimension tables. Where multiple fact tables are used, these are arranged as a fact constellation schema.
What is fact table in ETL?
A Fact Table is a central table in a star schema of a data warehouse. It is an important concept required for Data Warehousing and BI Certification. A fact table stores quantitative information for analysis and is often denormalized.
Can we join 2 fact tables?
The answer for both is "Yes, you can", but then also "No, you shouldn't". Joining fact tables is a big no-no for four main reasons: 1. Fact tables tend to have several keys (FK), and each join scenario will require the use of different keys.
Is fact table is Normalised?
The fact table is always DE-NORMALIZED.
What type of data is stored in fact table?
A fact table stores quantitative information for analysis and is often denormalized. 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.
What is fact table with example?
A fact table is found at the center of a star schema or snowflake schema surrounded by dimension tables. A fact table consists of facts of a particular business process e.g., sales revenue by month by product. Facts are also known as measurements or metrics. A fact table record captures a measurement or a metric.
How do you create a fact table?
Steps in designing Fact Table:Identify a business process for analysis(like sales).Identify measures or facts (sales dollar).Identify dimensions for facts(product dimension, location dimension, time dimension, organization dimension).List the columns that describe each dimension.More items...•
How many fact tables are there?
There are four types of fact tables: transaction, periodic snapshot, accumulating snapshot and factless fact tables.
Can fact table have primary key?
The fact table also has a primary (composite) key that is a combination of these four foreign keys. As a rule, each foreign key of the fact table must have its counterpart in a dimension table. Additionally, any table in a dimensional database that has a composite key must be a fact table.
What is a fact table vs a dimension table?
Fact table contains measurements, metrics, and facts about a business process while the Dimension table is a companion to the fact table which contains descriptive attributes to be used as query constraining.
How many fact tables are there?
There are four types of fact tables: transaction, periodic snapshot, accumulating snapshot and factless fact tables.
What are different types of fact tables dimension tables?
There are three types of fact tables:Transaction Fact Table. The transaction fact table is a basic approach to operate the businesses. ... Snapshot Fact Table. The snapshot fact table describes the state of things at a particular time and contains many semi-additive and non-additive facts. ... Accumulated Fact Sheet.
What are the main types of facts?
Types of Facts in Data WarehouseAdditive: Additive facts are facts that can be summed up through all of the dimensions in the fact table. ... Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others. ... Non-Additive:
What are different fact types?
There are three types of facts: Additive: Additive facts are facts that can be summed up through all of the dimensions in the fact table. Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others.
Fact Tables
Fact tables are the foundation of the data warehouse. They contain the fundamental measurements of the enterprise, and they are the ultimate target of most data warehouse queries. There is no point in hoisting fact tables up the flagpole unless they have been chosen to reflect urgent business priorities, have been carefully quality assured and […]
Keep to the Grain in Dimensional Modeling
When developing fact tables, aggregated data is NOT the place to start. To avoid “mixed granularity” woes including bad and overlapping data, stick to rich, expressive, atomic-level data that’s closely connected to the original source and collection process.
Who is Ralph Kimball?
Ralph Kimball is the founder of the Kimball Group and Kimball University where he has taught data warehouse design to more than 10,000 students. He is known for the best selling series of Toolkit books.
What is the difference between facts and context?
Facts are always surrounded by mostly textual context that’s true at the moment the fact is recorded. Facts are very specific, well-defined numeric attributes. By contrast, the context surrounding the facts is open-ended and verbose. It’s not uncommon for the designer to add context to a set of facts partway through the implementation.
What does snowflaking a dimension into third normal form do?
Snowflaking a dimension into third normal form, while not incorrect, destroys the ability to use bitmap indexes and increases the user-perceived complexity of the design.
How many levels of physical design are there in relational databases?
Actually, a real relational database has two levels of physical design. At the higher level, tables are explicitly declared together with their fields and keys. The lower level of physical design describes the way the bits are organized on the disk and in memory.
Can you add context to a set of facts?
It’s not uncommon for the designer to add context to a set of facts partway through the implementation. Although you could lump all context into a wide, logical record associated with each measured fact, you’ll usually find it convenient and intuitive to divide the context into independent logical clumps.
How to create a fact table?
Here is an overview of four steps to designing a fact table described by Kimball: 1 Choosing business process to a model – The first step is to decide what business process to model by gathering and understanding business needs and available data 2 Declare the grain – by declaring a grain means describing exactly what a fact table record represents 3 Choose the dimensions – once the grain of the fact table is stated clearly, it is time to determine dimensions for the fact table. 4 Identify facts – identify carefully which facts will appear in the fact table.
What are the different types of measures in a fact table?
Measure types. Fact table can store different types of measures such as additive, non-additive, semi-additive. Additive – As its name implied, additive measures are measures which can be added to all dimensions.
What is the primary key in a fact table?
But in a fact table, the primary key is almost always defined as a subset of the foreign keys supplied by the dimensions. In most environments this composite key will suffice as the primary key to the fact table.
Can you use surrogate keys in a fact table?
We only recommend creating surroga te keys for fact tables when certain special circumstance described in this design tip apply. As a quick reminder, surrogate keys are meaningless (aka ...
What is transaction fact table?
Transaction fact tables are easy to understand: a customer or business process does some thing; you want to capture the occurrence of that thing, and so you record a transaction in your data warehouse and you’re good to go.
Why do periodic snapshot tables have a large number of fields?
This is because any reasonably interesting metric may be shoved into the period table.
What is accumulating snapshot table?
Unlike periodic snapshot tables, accumulating snapshot tables are a little harder to explain. To understand why Kimball and his peers came up with this approach, it helps to understand a little about the kinds of questions that were being asked of business in the 90s, which was when the Data Warehouse Toolkit was first written.