The issue is related with Kimball Fact Tables types. I know the three types (that has metrics associated): Transactional, Periodic Snapshot and Accumulative Snapshot, and also others like FactLess, Factless Coverage and even Bridge Tables.
Full Answer
What is the Kimball Group?
Ralph Kimball introduced the data warehouse/business intelligence industry to dimensional modeling in 1996 with his seminal book, The Data Warehouse Toolkit . Since then, the Kimball Group has extended the portfolio of best practices.
What are some examples of Kimball’s approach to design?
A more general principle is to use technology to replace labor whenever possible. We have given you two examples of this: inventory modeling, and dealing with slowly changing dimensions. In both, Kimball’s approach demanded a level of manual engineering.
What is a Kimball-style star schema?
In a typical Kimball-style star schema, the fact table that is at the centre of your schema would consist of order transaction data. These are primarily numeric measures like order total, line item amounts, cost of goods sold, discount amounts applied, and so on.
Is Kimball’s approach to data modeling the same as Inmon?
But we should note that there is another approach to data modeling that is commonly mentioned in the same breath. This approach is known as Inmon data modeling, named after data warehouse pioneer Bill Inmon. Inmon’s approach was published in 1990, six years before Kimball’s.
What are the 3 types of fact tables?
These are:Transaction fact tables.Periodic snapshot tables, and.Accumulating snapshot tables.
What are different types of fact 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.
How many fact tables are there?
There are four types of fact tables: transaction, periodic snapshot, accumulating snapshot and factless fact tables.
What are the different types of dimension tables?
Types of Dimension TableSCD (Slowly Changing Dimensions) The dimension attributes that tend to change slowly with time rather than changing in a regular interval of time are called slowly changing dimensions. ... Conformed Dimension. ... Junk Dimension. ... Degenerate Dimension. ... Roleplay Dimension.
Which are two types of fact?
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.More items...
What are the 2 kinds of data that a fact tables contain?
Thus, the fact table consists of two types of columns. The foreign keys column allows joins with dimension tables, and the measures columns contain the data that is being analyzed.
Can we have 2 fact tables in star schema?
A schema can have one or more facts, but these facts are not linked by any key relationship. It is best practice not to join fact tables in a single query as you would whey querying a normalized/transactional database.
Which of the following are the types of facts?
We can divide the Facts in to these three types.Non-Additive.Semi-Additive.Additive.
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.
What are the 5 types of dimensions?
Top 9 Types of DimensionConformed Dimensions. A dimension is considered a conformed dimension and is found in many places. ... Role Playing Dimensions. ... Shrunken Dimensions. ... Static Dimensions. ... Degenerate Dimensions. ... Rapidly Changing Dimensions. ... Junk Dimensions. ... Inferred Dimensions.More items...
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.
What are the 3 types of SCD?
What are Slowly Changing DimensionsSCD TypeSummaryType 1Overwrite the changesType 2History will be added as a new row.Type 3History will be added as a new column.Type 4A new dimension will be added2 more rows•Sep 3, 2021
What are the different types of fact tables?
There are three fundamental types of fact tables in the data warehouse presentation area: transaction fact tables, periodic snapshot fact tables, and accumulating snapshot fact tables. Most DW/BI design teams are very familiar with transaction fact tables. They are the most common fact table type and are often the primary workhorse schema for many organizations. Many teams have also incorporated periodic snapshot fact tables in their presentation areas. Fewer organizations leverage the accumulating snapshot fact table. Design teams often don’t appreciate how an accumulating snapshot fact table can complement transaction and/or periodic snapshot fact tables.
What is a transaction fact table?
Transaction fact tables are an appropriate design response to business requirements that look for an understanding of the intensity or quantity of a business process. Transaction fact tables help answer the “how many?” question. For example, how many white sports utility vehicles (SUVs) were sold last week? What were the dollar sales? How many all-wheel drive vehicles were released by assembly into salable inventory? How many vehicles did we ship with a given carrier? How many vehicles were received by our dealers this month? Compared to last month, last quarter, or last year? There’s a reason transaction fact tables are the workhorse fact table type: they support critically important business requirements. On the other hand, transaction fact tables are less effective in answering questions regarding the state of our inventory levels or the speed/efficiency of the logistics pipeline. To support these business requirements, we look to other fact table types to complement transaction fact tables.
Why are transaction fact tables important?
There’s a reason transaction fact tables are the workhorse fact table type: they support critically important business requirements . On the other hand, transaction fact tables are less effective in answering questions regarding the state of our inventory levels or the speed/efficiency of the logistics pipeline.
Is it appropriate to implement all three fact tables?
Implementing all three fact table types is an appropriate response to the rich set of business requirements. Implementing only one or even only two of the fact table types would have made it very difficult, if not impossible, to support all the requirements.
What is Kimball's fourth type of fact table?
Second, I will note that Kimball recognises a fourth type of fact table — the timespan fact table — but it’s only used for special circumstances. We’ll leave that out of our discussion here.
What is the fact table in a Kimball star schema?
In a typical Kimball-style star schema, the fact table that is at the centre of your schema would consist of order transaction data. These are primarily numeric measures like order total, line item amounts, cost of goods sold, discount amounts applied, and so on.
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.
Why are periodic snapshot tables useful?
If you want to have an overview of the trend lines in the key performance indicators in your business, it helps to query against a periodic fact table.
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 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.
When was Kimball's Data Warehouse Toolkit first articulated?
Given all that has changed, it is remarkable how consistent and how useful Kimball’s ideas are since he first articulated them in 1996. The Data Warehouse Toolkit is a rich resource that every data analyst should mine — even if some of the recommendations no longer hold true in the presence of today’s technology.
What are the different types of fact tables?
There are three fundamental types of fact tables in the data warehouse presentation area: transaction fact tables, periodic snapshot fact tables, and accumulating snapshot fact tables. Most DW/BI design teams are very familiar with transaction fact tables. They are the most common fact table type and are often the primary workhorse schema for many organizations. ]
What is a fact table?
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 ]
What is a factless fact table?
Factless fact table are“fact tables that have no facts but captures the many-to-many relationship between dimension keys.” We’ve previously discussed factless fact tables to represent events or coverage information. An event-based factless fact table is student attendance information; the grain of the fact table is one row per student each day. A typical coverage factless fact ]
What are descriptive dimensions?
For most subject areas, it’s pretty easy to identify the major dimensions: Product, Customer Account, Student, Employee, and Organization are all easily understood as descriptive dimensions. A store’s sales, a telecommunication company’s phone calls, and a college’s course registrations are all clearly facts.
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.
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 is transactional fact table?
Transactional – Transactional fact table is the most basic one that each grain associated with it indicated as “one row per line in a transaction”, e.g., every line item appears on an invoice. Transaction fact table stores data of the most detailed level, therefore, it has a high number of dimensions associated with it.
When to choose dimensions for fact table?
Choose the dimensions – once the grain of the fact table is stated clearly, it is time to determine dimensions for the fact table.
Where is the fact table in a data warehouse?
A fact table is used in the dimensional model in data warehouse design. A fact table is found at the center of a star schema or snowflake schema surr ounded by dimension tables.
What are some examples of Kimball's approach?
We have given you two examples of this: inventory modeling, and dealing with slowly changing dimensions. In both, Kimball’s approach demanded a level of manual engineering. The contemporary approach is to simply rely on the power of modern data infrastructure to render such manual activities irrelevant.
Why Was Kimball’s Approach Needed?
Before we discuss if these techniques are applicable today, we must ask: why were these data modeling techniques introduced in the first place? Answering this question helps us because we may now evaluate if the underlying reasons have changed.
Why did Kimball use the star schema?
Data warehouse designers before the Kimball era would often come up with normalized schemas. This made query writing very complicated, and made it more difficult for business intelligence teams to deliver value to the business quickly and reliably. Kimball was amongst the first to formally realize that denormalized data worked better for analytical workloads compared to normalized data. His notion of the star schema, alongside the ‘four steps’ we discussed earlier in this section, turned his approach into a repeatable and easily applicable process.
What is Kimball's dimensional modeling approach?
Performant — Kimball’s dimensional modeling approach was developed when the majority of analytical systems were run on relational database management systems (RDBMSes). The star schema is particularly performant on RDBMSes, as most queries end up being executed using the ‘star join’, which is a Cartesian product of all the dimensional tables.
What is Kimball's solution to snapshotting?
Therefore, he dedicates an entire chapter to discuss various techniques to get around this problem. The main solution Kimball proposes is to use ETL tools to create ‘snapshot’ fact tables, that are basically aggregated inventory moves for a certain time period. This snapshotting action is meant to occur on a regular basis.
What is Kimball's approach to star schema?
Let’s give credit where credit is due: Kimball’s ideas around the star schema, his approach of using denormalized data, and the notion of dimension and fact tables are powerful, time-tested ways to model data for analytical workloads. We use it internally at Holistics, and we recommend you do the same.
What is a star schema?
The star schema is a particular way of organizing data for analytical purposes. It consists of two types of tables: A fact table, which acts as the primary table for the schema. A fact table contains the primary measurements, metrics, or ‘facts’ of a business process. Many dimension tables associated with the fact table.
What is a fact table?
fact table contains the numeric measures produced by an operational measurement event in the real world. At the lowest grain, a fact table row corresponds to a measurement event and vice versa. Thus the fundamental design of a fact table is entirely based on a physical activity and is not influenced by the eventual reports that may be produced. In addition to numeric measures, a fact table always contains foreign keys for each of its associated dimensions, as well as optional degenerate dimension keys and date/time stamps. Fact tables are the primary target of computations and dynamic aggregations arising from queries.
How many times can a physical dimension be referenced in a fact table?
single physical dimension can be referenced multiple times in a fact table, with each reference linking to a logically distinct role for the dimension. For instance, a fact table can have several dates, each of which is represented by a foreign key to the date dimension. It is essential that each foreign key refers to a separate view of the date dimension so that the references are independent. These separate dimension views (with unique attribute column names) are called roles.
What is a conformed dimension table?
Dimension tables conform when attributes in separate dimension tables have the same column names and domain contents. Information from separate fact tables can be combined in a single report by using conformed dimension attributes that are associated with each fact table. When a conformed attribute is used as the row header (that is, the grouping column in the SQL query), the results from the separate fact tables can be aligned on the same rows in a drill-across report. This is the essence of integration in an enterprise DW/ BI system. Conformed dimensions, defined once in collaboration with the business’s data governance representatives, are reused across fact tables; they deliver both analytic consistency and reduced future development costs because the wheel is not repeatedly re-created
What is a natural key?
Natural keys created by operational source systems are subject to business rules outside the control of the DW/BI system. For instance, an employee number (natural key) may be changed if the employee resigns and then is rehired. When the data warehouse wants to have a single key for that employee, a new durable key must be created that is persistent and does not change in this situation. This key is sometimes referred to as a durable supernatural key. The best durable keys have a format that is independent of the original business process and thus should be simple integers assigned in sequence beginning with 1. While multiple surrogate keys may be associated with an employee over time as their profile changes, the durable key never changes.
What is dimension table?
dimension table is designed with one column serving as a unique primary key. This primary key cannot be the operational system’s natural key because there will be multiple dimension rows for that natural key when changes are tracked over time. In addition, natural keys for a dimension may be created by more than one source system, and these natural keys may be incompatible or poorly administered. The DW/BI system needs to claim control of the primary keys of all dimensions; rather than using explicit natural keys or natural keys with appended dates, you should create anonymous integer primary keys for every dimension. These dimension surrogate keys are simple integers, assigned in sequence, starting with the value 1, every time a new key is needed. The date dimension is exempt from the surrogate key rule; this highly predictable and stable dimension can use a more meaningful primary key.
What is aggregate fact table?
Aggregate fact tables are simple numeric rollups of atomic fact table data built solely to accelerate query performance. These aggregate fact tables should be available to the BI layer at the same time as the atomic fact tables so that BI tools smoothly choose the appropriate aggregate level at query time. This process, known as aggregate navigation, must be open so that every report writer, query tool, and BI application harvests the same performance benefits. A properly designed set of aggregates should behave like database indexes, which accelerate query performance but are not encountered directly by the BI applications or business users. Aggregate fact tables contain foreign keys to shrunken conformed dimensions, as well as aggregated facts created by summing measures from more atomic fact tables. Finally, aggregate OLAP cubes with summarized measures are frequently built in the same way as relational aggregates, but the OLAP cubes are meant to be accessed directly by the business users.
Where should freeform comments be stored?
Rather than treating freeform comments as textual metrics in a fact table, they should be stored outside the fact table in a separate comments dimension (or as attributes in a dimension with one row per transaction if the comments’ cardinality matches the number of unique transactions) with a corresponding foreign key in the fact table.
An Example of A Fact Table
Measure Types
- A fact table can store different types of measures such as additive, non-additive, semi-additive. 1. Additive– As its name implied, additive measures are measures that can be added to all dimensions. 2. Non-additive– different from additive measures, non-additive measures are measures that cannot be added to all dimensions. 3. Semi-additive– semi-a...
Types of Fact Tables
- All fact tables are categorized by the three most basic measurement events: 1. Transactional– Transactional fact table is the most basic one that each grain associated with it indicated as “one row per line in a transaction”, e.g., every line item appears on an invoice. Transaction fact table stores data of the most detailed level, therefore, it has a high number of dimensions associated …
Designing Fact Table Steps
- 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…