What is a Factless Fact Table? Where we use Factless Fact?

We know that fact table is a collection of many facts and measures having multiple keys joined with one or more dimension tables. Facts contain both numeric and additive fields.But fact-less fact table are different from all these.
A fact-less fact table is fact table that does not contain fact. They contain only dimensional keys and it captures events that happen only at information level but not included in the calculations level, just an information about an event that happen over a period.
A fact-less fact table captures the many-to-many relationships between dimensions, but contains no numeric or textual facts. They are often used to record events or coverage information. Common examples of fact-less fact tables include:

• Identifying product promotion events (to determine promoted products that din’t sell)
• Tracking student attendance or registration events
• Tracking insurance-related accident events
• Identifying building, facility, and equipment schedules for a hospital or university

Fact-less fact tables are used for tracking a process or collecting stats. They are called so because, the fact table does not have aggregate numeric values or information.There are two types of fact-less fact tables: those that describe events, and those that describe conditions. Both may play important roles in your dimensional models.

Fact-less fact tables for Events

The first type of fact-less fact table is a table that records an event. Many event-tracking tables in dimensional data warehouses turn out to be fact-less. Sometimes there seem to be no facts associated with an important business process. Events or activities occur that you wish to track, but you find no measurements. In situations like this, build a standard transaction-grained fact table that contains no facts.
For eg.

The above fact is used to capture the leave taken by an employee.Whenever an employee takes leave a record is created with the dimensions.Using the fact FACT_LEAVE we can answer many questions like

• Number of leaves taken by an employee
• The type of leave an employee takes
• Details of the employee who took leave

Fact-less fact tables for Conditions

Fact-less fact tables are also used to model conditions or other important relationships among dimensions. In these cases, there are no clear transactions or events.It is used to support negative analysis report. For example a Store that did not sell a product for a given period.  To produce such report, you need to have a fact table to capture all the possible combinations.  You can then figure out what is missing.

e.g.: fact_promo gives the information about the products which have promotions but still did not sell.

 This  fact answers the below questions:

• To find out products that have promotions.
• To find out products that have promotion that sell.
• The list of products that have promotion but did not sell.

This kind of fact-less fact table is used to track conditions, coverage or eligibility.  In Kimball terminology, it is called a "coverage table."

Note:

We may have the question that why we cannot include these information in the actual fact table .The problem is that if we do so then the fact size will increase enormously .

Fact-less fact table is crucial in many complex business processes. By applying you can design a dimensional model that has no clear facts to produce more meaningful information for your business processes. Fact-less fact table itself can be used to generate the useful reports.

What is an OLAP and Explain OLAP's advantages with example?

OLAP (or Online Analytical Processing) has been growing in popularity due to the increase in data volumes and the recognition of the business value of analytics. It uses database tables (fact and dimension tables) to enable multidimensional viewing, analysis and querying of large amounts of data. E.g. OLAP technology could provide management with fast answers to complex queries on their operational data or enable them to analyze their company's historical data for trends and patterns.

OLAP allows business users to slice and dice data at will. Normally data in an organization is distributed in multiple data sources and are incompatible with each other. A retail example: Point-of-sales data and sales made via call-center or the Web are stored in different location and formats. It would a time consuming process for an executive to obtain OLAP reports such as - What are the most popular products purchased by customers between the ages 15 to 30?

Part of the OLAP implementation process involves extracting data from the various data repositories and making them compatible. Making data compatible involves ensuring that the meaning of the data in one repository matches all other repositories. An example of incompatible data: Customer ages can be stored as birth date for purchases made over the web and stored as age categories (i.e. between 15 and 30) for in store sales.The major OLAP vendors are Hyperion, Cognos, Business Objects, MicroStrategy.
OLAPs are designed to give an overview analysis of what happened. Hence the data storage (i.e. data modeling) has to be set up differently. The most common method is called the star design.

The central table in an OLAP start data model is called the fact table. The surrounding tables are called the dimensions. Using the above data model, it is possible to build reports that answer questions such as:

• The supervisor that gave the most discounts.
• The quantity shipped on a particular date, month, year or quarter.
• In which pincode did product A sell the most.

To obtain answers, such as the ones above, from a data model OLAP cubes are created. OLAP cubes are not strictly cuboids - it is the name given to the process of linking data from the different dimensions. The cubes can be developed along business units such as sales or marketing.Or a giant cube can be formed with all the dimensions.

OLAP can be a valuable and rewarding business tool. Aside from producing reports, OLAP analysis can aid an organization evaluate balanced scorecard targets.

Business intelligence refers to computer-based methods for identifying and extracting useful information from business data. OLAP (online analytical processing) as the name suggest is a compilation of ways to query multi-dimensional databases.

OLAP is a class of systems, which provide answers to multi-dimensional queries.Typically OLAP is used for marketing, budgeting, forecasting and similar applications. It goes without saying that the databases used for OLAP are configured for complex and ad-hoc queries with a quick performance in mind. Typically a matrix is used to display the output of an OLAP. The rows and columns are formed by the dimensions of the query. They often use methods of aggregation on multiple tables to obtain summaries.For example,it can be used to find out about the sales of this year in Wal-Mart compared to last year? What is the prediction on the sales in the next quarter? What can be said about the trend by looking at the percentage change?
OLAP tools provides multidimensional data analysis and they provide summaries of the data but contrastingly, data mining focuses on ratios, patterns and influences in the set of data.