Dimension –

A dimension table typically has two types of columns, primary keys to fact tables and textual\descreptive data.

Fact –
A fact table typically has two types of columns, foreign keys to dimension tables and measures those that contain numeric facts. A fact table can contain fact’s data on detail or aggregated level.

 

Types of Dimensions –

Slowly Changing Dimensions:

 Attributes of a dimension that would undergo changes over time. It depends on the business requirement whether particular attribute history of changes should be preserved in the data warehouse. This is called a Slowly Changing Attribute and a dimension containing such an attribute is called a Slowly Changing Dimension.

 

Rapidly Changing Dimensions:

A dimension attribute that changes frequently is a Rapidly Changing Attribute. If you don’t need to track the changes, the Rapidly Changing Attribute is no problem, but if you do need to track the changes, using a standard Slowly Changing Dimension technique can result in a huge inflation of the size of the dimension. One solution is to move the attribute to its own dimension, with a separate foreign key in the fact table. This new dimension is called a Rapidly Changing Dimension.

 

Junk Dimensions:

A junk dimension is a single table with a combination of different and unrelated attributes to avoid having a large number of foreign keys in the fact table. Junk dimensions are often created to manage the foreign keys created by Rapidly Changing Dimensions.

 

Inferred Dimensions:

While loading fact records, a dimension record may not yet be ready. One solution is to generate an surrogate key with Null for all the other attributes. This should technically be called an inferred member, but is often called an inferred dimension.

 

Conformed Dimensions:

A Dimension that is used in multiple locations is called a conformed dimension. A conformed dimension may be used with multiple fact tables in a single database, or across multiple data marts or data warehouses.

 

Degenerate Dimensions:

 A degenerate dimension is when the dimension attribute is stored as part of fact table, and not in a separate dimension table. These are essentially dimension keys for which there are no other attributes. In a data warehouse, these are often used as the result of a drill through query to analyze the source of an aggregated number in a report. You can use these values to trace back to transactions in the OLTP system.

 

Role Playing Dimensions:

A role-playing dimension is one where the same dimension key — along with its associated attributes — can be joined to more than one foreign key in the fact table. For example, a fact table may include foreign keys for both Ship Date and Delivery Date. But the same date dimension attributes apply to each foreign key, so you can join the same dimension table to both foreign keys. Here the date dimension is taking multiple roles to map ship date as well as delivery date, and hence the name of Role Playing dimension.

 

Shrunken Dimensions:

A shrunken dimension is a subset of another dimension. For example, the Orders fact table may include a foreign key for Product, but the Target fact table may include a foreign key only for ProductCategory, which is in the Product table, but much less granular. Creating a smaller dimension table, with ProductCategory as its primary key, is one way of dealing with this situation of heterogeneous grain. If the Product dimension is snowflaked, there is probably already a separate table for ProductCategory, which can serve as the Shrunken Dimension.

 

Static Dimensions:

Static dimensions are not extracted from the original data source, but are created within the context of the data warehouse. A static dimension can be loaded manually — for example with Status codes — or it can be generated by a procedure, such as a Date or Time dimension.

 

Types of Facts –

 

Additive:

Additive facts are facts that can be summed up through all of the dimensions in the fact table. A sales fact is a good example for additive fact.

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.
Eg: Daily balances fact can be summed up through the customers dimension but not through the time dimension.

Non-Additive:

Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table.
Eg: Facts which have percentages, ratios calculated.

 

Factless Fact Table:

In the real world, it is possible to have a fact table that contains no measures or facts. These tables are called “Factless Fact tables”.

Eg: A fact table which has only product key and date key is a factless fact. There are no measures in this table. But still you can get the number products sold over a period of time.

Based on the above classifications, fact tables are categorized into two:

Cumulative:

This type of fact table describes what has happened over a period of time. For example, this fact table may describe the total sales by product by store by day. The facts for this type of fact tables are mostly additive facts. The first example presented here is a cumulative fact table.

Snapshot:

This type of fact table describes the state of things in a particular instance of time, and usually includes more semi-additive and non-additive facts. The second example presented here is a snapshot fact table.

1) There are standard type of joins and framework.

All dimensions can be thought of as symmetrically equal entry points into the fact table.

The logical design is independent of expected query patterns. The user interfaces are symmetrical, the query strategies are symmetrical, and the SQL generated against the dimensional model is symmetrical.

In other words,

• You will never find attributes in fact tables and facts in dimension tables.
• If you see a non-fact field in the fact table, you can assume that it is a key to a dimension table

2) it is smoothly extensible to accommodate unexpected new data elements and new design decisions.

First, all existing tables (both fact and dimension) can be changed in place by simply adding new data rows in the table. Data should not have to be reloaded. Typically, No query tool OR reporting tool needs to be reprogrammed to accommodate the change. All old applications continue to run without yielding different results. You can, respectively, make the following graceful changes to the design after the data warehouse is up and running by:

• Adding new unanticipated facts (that is, new additive numeric fields in the fact table), as long as they are consistent with the fundamental grain of the existing fact table.
• Adding completely new dimensions, as long as there is a single value of that dimension defined for each existing fact record
• Adding new, unanticipated dimensional attributes.
• Breaking existing dimension records down to a lower level of granularity from a certain point in time forward.

3) There is a body of standard approaches for handling common modeling situations in the business world.

Each of these situations has a well-understood set of alternatives that can be specifically programmed in report writers, query tools, and other user interfaces. These modeling situations include:

• Slowly changing dimensions, where a ‘constant’ dimension such as Product OR Customer actually evolves slowly and asynchronously. Dimensional modeling provides specific techniques for handling slowly changing dimensions, depending on the business environment.
• Heterogeneous products, where a business such as a bank needs to:
  • track a number of different lines of business together within a single common set of attributes and facts, but at the same time..
  • it needs to describe and measure the individual lines of business in highly idiosyncratic ways using incompatible measures.

The ODS can be seen as an intermediate stage between the sources and the DW, although authors also propose that it can be used as a database for operational processing. It contains integrated data, but this data is at detail level and it is only current data. Therefore we can think that with this architecture we are dividing the transformation work into two phases: in the first phase the main task is integration, and in the second phase the rest of the data transformation work is done.

Target data can be stored in:

Single data warehouse	Data marts with logical data chunks	Both data warehouse and data marts
The data warehouse has all the information of the enterprise in a historical archival.	Data Marts are proposed as logical subsets of the complete DW. They should be consistent in their data representation in order to assure DW robustness.	Data warehouses are used where interdependency of data marts is needed. Data marts are used for analysis of components

 

The data models that are used for DWs are Multidimensional Model and Relational Model.

 

Two different approaches for Relational DW design: one that applies dimensional modelling techniques, and another that bases mainly in the concept of materialized views.

Dimensional models represent data with a “cube” structure , making more compatible logical data representation with OLAP data management.

The objectives of dimensional modelling are:

• To produce database structures that are easy for end-users to understand and write queries against, and
• To maximise the efficiency of queries. It achieves these objectives by minimising the number of tables and relationships between them.