When reading a book for business objects, I came across the term- fact table and dimension table.
I am trying to understand what is the different between Dimension table and Fact table?
I read couple of articles on the internet but I was not able to understand clearly..
Any simple example will help me to understand better?
In Data Warehouse Modeling, a star schema and a snowflake schema consists of Fact and Dimension tables.
Fact Table:
It contains all the primary keys of the dimension and associated
facts or measures(is a property on which calculations can be made) like quantity sold, amount sold and average sales.
Dimension Tables:
Dimension tables provides descriptive information for all the measurements recorded in fact table.
Dimensions are relatively very small as comparison of fact table.
Commonly used dimensions are people, products, place and time.
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This appears to be a very simple answer on how to differentiate between fact and dimension tables!
It may help to think of dimensions as things or objects. A thing such
as a product can exist without ever being involved in a business
event. A dimension is your noun. It is something that can exist
independent of a business event, such as a sale. Products, employees,
equipment, are all things that exist. A dimension either does
something, or has something done to it.
Employees sell, customers buy. Employees and customers are examples of
dimensions, they do.
Products are sold, they are also dimensions as they have something
done to them.
Facts, are the verb. An entry in a fact table marks a discrete event
that happens to something from the dimension table. A product sale
would be recorded in a fact table. The event of the sale would be
noted by what product was sold, which employee sold it, and which
customer bought it. Product, Employee, and Customer are all dimensions
that describe the event, the sale.
In addition fact tables also typically have some kind of quantitative
data. The quantity sold, the price per item, total price, and so on.
Source:
http://arcanecode.com/2007/07/23/dimensions-versus-facts-in-data-warehousing/
This is to answer the part:
I was trying to understand whether dimension tables can be fact table
as well or not?
The short answer (INMO) is No.That is because the 2 types of tables are created for different reasons. However, from a database design perspective, a dimension table could have a parent table as the case with the fact table which always has a dimension table (or more) as a parent. Also, fact tables may be aggregated, whereas Dimension tables are not aggregated. Another reason is that fact tables are not supposed to be updated in place whereas Dimension tables could be updated in place in some cases.
More details:
Fact and dimension tables appear in a what is commonly known as a Star Schema. A primary purpose of star schema is to simplify a complex normalized set of tables and consolidate data (possibly from different systems) into one database structure that can be queried in a very efficient way.
On its simplest form, it contains a fact table (Example: StoreSales) and a one or more dimension tables. Each Dimension entry has 0,1 or more fact tables associated with it (Example of dimension tables: Geography, Item, Supplier, Customer, Time, etc.). It would be valid also for the dimension to have a parent, in which case the model is of type "Snow Flake". However, designers attempt to avoid this kind of design since it causes more joins that slow performance. In the example of StoreSales, The Geography dimension could be composed of the columns (GeoID, ContenentName, CountryName, StateProvName, CityName, StartDate, EndDate)
In a Snow Flakes model, you could have 2 normalized tables for Geo information, namely: Content Table, Country Table.
You can find plenty of examples on Star Schema. Also, check this out to see an alternative view on the star schema model Inmon vs. Kimball. Kimbal has a good forum you may also want to check out here: Kimball Forum.
Edit: To answer comment about examples for 4NF:
Example for a fact table violating 4NF:
Sales Fact (ID, BranchID, SalesPersonID, ItemID, Amount, TimeID)
Example for a fact table not violating 4NF:
AggregatedSales (BranchID, TotalAmount)
Here the relation is in 4NF
The last example is rather uncommon.
Super simple explanation:
Fact table: a data table that maps lookup IDs together. Is usually one of the main tables central to your application.
Dimension table: a lookup table used to store values (such as city names or states) that are repeated frequently in the fact table.
Dimension table
Dimension table is a table which contain attributes of measurements stored in fact tables. This table consists of hierarchies, categories and logic that can be used to traverse in nodes.
Fact table contains the measurement of business processes, and it contains foreign keys for the dimension tables.
Example – If the business process is manufacturing of bricks
Average number of bricks produced by one person/machine – measure of the business process
a Fact = an action: a sale, a transaction, an access
a Dimension = an object: a seller, a customer, a date, a price
Then...
Facts references dimensions for: when, where, what, who, how
The real interesting thing is deciding whether an attribute should be a dimension or a fact. For example, the price of each item in an order, or, the maximum amount of a insurance recorded in a contract. There are no generally correct way to approach these, only ones that make sense in the context.
PS: If I were to create those jargons I would prefer Log table and Object table.
In the simplest form, I think a dimension table is something like a 'Master' table - that keeps a list of all 'items', so to say.
A fact table is a transaction table which describes all the transactions. In addition, aggregated (grouped) data like total sales by sales person, total sales by branch - such kinds of tables also might exist as independent fact tables.
From my point of view,
Dimension table : Master Data
Fact table : Transactional Data
The fact table mainly consists of business facts and foreign keys that refer to primary keys in the dimension tables. A dimension table consists mainly of descriptive attributes that are textual fields.
A dimension table contains a surrogate key, natural key, and a set of attributes. On the contrary, a fact table contains a foreign key, measurements, and degenerated dimensions.
Dimension tables provide descriptive or contextual information for the measurement of a fact table. On the other hand, fact tables provide the measurements of an enterprise.
When comparing the size of the two tables, a fact table is bigger than a dimensional table. In a comparison table, more dimensions are presented than the fact tables. In a fact table, less numbers of facts are observed.
The dimension table has to be loaded first. While loading the fact tables, one should have to look at the dimension table. This is because the fact table has measures, facts, and foreign keys that are the primary keys in the dimension table.
Read more: Dimension Table and Fact Table | Difference Between | Dimension Table vs Fact Table http://www.differencebetween.net/technology/hardware-technology/dimension-table-and-fact-table/#ixzz3SBp8kPzo
For Relation database users, Dimension is equivalent to Master Table.
Fact is equivalent to Transaction table.
Dimension table : It is nothing but we can maintains information about the characterized date called as Dimension table.
Example : Time Dimension , Product Dimension.
Fact Table : It is nothing but we can maintains information about the metrics or precalculation data.
Example : Sales Fact, Order Fact.
Star schema : one fact table link with dimension table form as a Start Schema.
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Related
I am trying to understand how fact tables are form in relation to the dimension tables.
E.g. Sale Fact Table
For there is a query for Sale of product by year/month/week/day, do I create a dimension for each type of period: Dim_Year, Dim_Month, Dim_Week and Dim_Day, each with their own respective keys?
Or is it possible to just use one dimension for all periods: Dim_Date and only have one date key?
Another area I am confused about is that why do some fact tables not contain their own ID? E.g. Sale fact table does not have SaleID included in the fact table.
Sale Fact Table Textbook Example
DATES
Your date dimension needs to correspond to the grain of your fact table. So if you had daily sales you would have a Dim_Day, weekly sales you would have a Dim_Week, etc.
You would normally have multiple date dimensions (at different grains) in your data warehouse as you would have facts at different date grains.
Each date dimension would hold hold attributes applicable to levels higher up in the date hierarchy. So a Dim_Day might hold day, week, month, year attributes; Dim_Month might hold month, quarter and year attributes, etc.
PRIMARY KEYS
Primary keys are rarely (never?) a technical requirement when creating tables in a database i.e. you can create a table without defining a PK. So you need to consider why we normally (at least in OLTP DBs) include PKs. Common reasons include:
To easily identify an individual record
To ensure that duplicate records (those with the same PK value) are
not created
So there are good reasons for creating PKs, however there are cost overheads e.g. the PK needs to be checked every time a new record is inserted into the table.
In a dimensional model where you are performing bulk inserts/updates, having PKs would cause a significant performance hit. Additionally, the insert logic/checks should always be implemented in your ETL processes so there is no need to include these types of checks/constraints in the DB itself.
Fact tables do have a primary key but it is often implicit rather than explicit - so a group of the FKs in the fact table uniquely identify each record. This compound PK may be documented but is is never enabled/implemented.
Occasionally a fact table will have an explicit, single column, PK. This is normally used when the fact table needs to be updated and its implicit PK involves a large number of columns. There is normally logic required to identify the record to be updated using its FKs but this returns the PK; then the update statement just has a clause like this:
WHERE table_pk = 12345678
rather than having to include all the columns in the implicit PK:
WHERE table_sk1 = 1234
AND table_sk2 = 5678
AND table_sk3 = 9876
....
Hope this helps?
What is the line that you should draw when normalising data, in terms of data duplication? i.e would you say that 2 employees who share the same birthday or have the same timestamp for a shift is data duplication? and therefore should be placed into another data table?
Birth date has full and non-transitive dependency to a person which means that it should be stored within the same table where you keep your employees and it would comply with third normal form (3NF).
Work shifts are not an attribute of an employee which means that they are a different entity and stay in relation with employee entity.
There is no a particular 'limit' when following the normalisation to data, since the main restriction that is given for every relational database table is to have an unique parimary key. Hence, if all other columns contain the same data, but the primary key is still different, it is a different row of a table.
The actual restrictions can come in two form. One is either the programming or systhematic approach, where the restriction on what kind of data is inputed is given from a program which interacts with the database or already defined script handed down physically for the admin of the database.
Other, more database-orriented approach would be to create primary keys composed of multiple columns. That way a row is unique only if for both columns the data is unique. It should be noted that a primary key is not necessary the same as an unique key, which should be different for every instance.
You have misunderstood what normalization does.
Two attributes having the same value (i.e. two employees having the same birthday) is not redundancy.
Rather having the same attribute in the two tables (i.e. two tables having birthday column, therefore repeating every employee's birthday information) is.
Normalization is a quality decision and denormalization is a performance decision. For my school projects, my teachers recommended me to normalize at least till 3NF. So that may be a good guideline.
We recently started working on our Data warehouse. We have Technicians, Salespersons, Date, Branch, Customer as our dimensions. We also have transactional tables in OLTP such as Sales Orders, Agreements, Which are referenced to each other in some situations. I'm planning to put sales order, Agreements info in Fact tables. So, that I would like to reference the all the above mentioned dimensions in both fact tables. But, here comes my problem.
Sales orders and service agreements need to referenced with each other. In most cases, Agreements information need to be referenced in Sales orders. Can I reference two fact tables each other in fact table? The sales orders table in OLTP consists Million records, and Agreement table holds half million records(minimum). Can you let me know if I can reference these two in fact table?
Do you have something like an AgreementID which can be added to each fact table and then used to drill across? This is a degenerate dimension - a dimension key without a dimension table.
I know it is a big and general question. Let me describe what I am looking for.
In big projects, we have some entities with many properties. (Many is over 100 properties for just a specific entity.) These properties have one to one relation. By the time goes, these tables with many columns are really big problems for maintenance and further development.
As you think, these 90 columns is created in a time with many projects. Not a single project. Therefore, requirements affect the table design in a wide time duration.
i.e. : There is a table to store information of payments between banks in global.
Some columns are foreign keys of others.(Customer, TransferType etc.)
Some columns are parameters of current payment. (IsActive, IsLoaded, IsOurCustomer etc.)
Some columns are fields of payment. (Information Bank, Receiver Bank etc.)
and so on.
These fields are always counting and now we have about 90 columns with one to one relation.
What are the concerns to divide a table to smaller tables. I know normalization rules and I am not interested it. (Already duplicated columns are normalized)
I try to find some patterns or some rules to divide a table which has one to one relation among columns.
If all of the columns are only dependent on the primary table key and are not repeating (phone1, phone2) they should be part of the same table. If you split a table you will have to do joins when you need all the columns of the table. If many of the values are null you may investigate the use of sparse columns (which don't take up any room if they have a null value).
When reading a book for business objects, I came across the term- fact table and dimension table. Is this the standard thing for all the database that they all have fact table and dimension table or is it just for business object design? I am looking for an explanation which differentiates between two and how they are related.
Edited:
Why cannot a query just get the required data from the fact table? What happens if all the information are stored in one fact table alone? What advantages we get by creating a separate fact and dimension table and joining it?
Sorry for too many questions at a time but I would like to know about the inter-relations and whys.
Dimension and Fact are key terms in OLAP database design.
Fact table contains data that can be aggregate.
Measures are aggregated data expressions (e. Sum of costs, Count of calls, ...)
Dimension contains data that is use to generate groups and filters.
Fact table without dimension data is useless. A sample: "the sum of orders is 1M" is not information but "the sum of orders from 2005 to 2009" it is.
They are a lot of BI tools that work with these concepts (e.g. Microsft SSAS, Tableau Software) and languages (e. MDX).
Some times is not easy to know if a data is a measure or a dimension. For example, we are analyzing revenue, both scenarios are possibles:
3 measures: net profit , overheads , interest
1 measure: profit and 1 dimension: profit type (with 3 elements: net, overhead, interest )
The BI analyst is who determines what is the best design for each solution.
EDITED due to the question also being edited:
An OLAP solution usually has a semantic layer. This layer provides to the OLAP tool information about: which elements are fact data, which elements are dimension data and the table relationships. Unlike OLTP systems, it is not required that an OLAP database is properly normalized. For this reason, you can take dimension data from several tables including fact tables. A dimension that takes data from a fact table is named Fact Dimension or Degenerate dimension.
They are a lot of concepts that you should keep in mind when designing OLAP databases: "STAR Schema", "SNOWFLAKE Schema", "Surrogate keys", "parent-child hierarchies", ...
That's a standard in a datawarehouse to have fact tables and dimension tables. A fact table contains the data that you are measuring, for instance what you are summing. A dimension table is a table containing data that you don't want to constantly repeat in the fact table, for example, product data, statuses, customers etc. They are related by keys: in a star schema, each row in the fact table contains a the key of a row in the dimension table.