BI Terminologies

Agenda OLAP Data Warehouse Data Warehouse design Cube Dimensions Measures Facts

Agenda Cont’d Measure Groups Calculated Measures Measure Expression Hierarchies Dimension Members Discretization Calculated Members Custom Member Formulas

Agenda Cont’d Cell and Tuple Set and Named Set KPI MDX Dimension Types Dimension relationship to Facts Summary Questions

OLAP

Data Warehouse Repository of an organization’s electrically stored data. Designed to facilitate reporting and analysis. How is it used in the BI solution?

Data Warehouse design Star Schema: Consists of few fact tables, referencing directly any number of dimension tables

Data Warehouse design Cont’d Snowflake Schema: Consists of a centralized fact tables which are connected to multiple dimensions, and these dimensions are normalized into multiple related tables

Data Warehouse design Cont’d Combination Schema: Mix approach leading to some dimension tables being completely a star schema while other dimension tables are snow flake schema to save space.

Cube Data structure that allows fast analysis of data. Cube Operations: Slice (Where) Dice (Select) Drill up/down Types to save Cube aggregations: MOLAP (high performance, high storage) ROLAP (less performance, better storage) HOLAP (benefit from both)

Dimensions The specific data which the user will be concerned to view his data by this categorization

Measures The fields in which the system is concerned to measure.

Facts Group of measures that can be categorized by dimensions.

Example If we have a cube connected to a data source that contains the sales amount data and date time data, and the user want to view the sales amount by time, (ex. view the sales amount per month). Then we shall have a dimension “” as this what data would be categorized by. Then we will have a measure which will be the “”

Example If we have a cube connected to a data source that contains the sales amount data and date time data, and the user want to view the sales amount by time, (ex. view the sales amount per month). Then we shall have a dimension “Date Time” as this what data would be categorized by. Then we will have a measure which will be the “Sales amount” And in the fact table we will have each measure labeled to a certain period of time using foreign keys.

Measure Groups It is a group of measures; a fact is considered a measure group.

Calculated Measure A measure that is calculated by a formula, not by a direct aggregation. Example: If we have a measure to calculate the sum of salaries of employees, but we want to get the salaries including the 10% taxes, then we will create a calculated measure which will take the value of the salary and multiply it by 0.1. Also it can be used if we want to multiply 2 measures with each other; this also is considered a calculated measure

Measure Expression Works on the least level of members Formula is executed before the aggregation process

Example Calculated Measure Vs Measure Expression Branches B1 SE1 SE2 B2 SE3 SE4

Example Calculated Measure Vs Measure Expression Cont’d Quotes Q1 [1000$] SE1 [20%] SE3 [80%] Q2[1000$] SE1 [10%] SE2 [10%] SE3 [40%] SE4 [40%] Total amount for B1???

Example Calculated Measure Vs Measure Expression Cont’d It will aggregate first, then execute the multiplication so for B1 we will have the total amount (20%+10%+10%) * (2000$) which will be 800$. Measure expression: It will execute the multiplication on the lowest level then aggregate, so for B1 we will have the total amount (20% * 1000$) + (10% * 1000) + (10% * 1000) which will be 400$. And this is the correct value.

Attribute It is the columns within a dimension table.

Hierarchies It is a grouping of attributes ordered to reflect their relationship with other attributes Example: In the time dimension, we could make the following hierarchy ‘YearQuarterMonthWeekDay’, which means that every year will be divided into quarters, and each quarter will be divided into months, and so on

Dimension Members It is the value of each cell in the dimension table Example: In time dimension, in the year attribute, we have ‘2005’,’2006’,’2007’,.. As member of this attribute.

Discretization The process of grouping members of an attribute into a number of member groups. Example: If we have an attribute “City”, that contain a large number of members (ex. 500,000 city) we can discretize this attribute into groups to facilitate viewing of this attribute (ex. Cities from A-B, then C-D,.. and so on.

Calculated Members It is a member that is calculated by a formula not as the normal members of the attribute. Example: “Year to Date” added to the members of the attribute, you should be aware that the calculated members have different behavior than the normal members, so they should be tested along with the normal members.

Custom Member Formulas It is a member value that is calculated or set, for certain conditions, it will not be generic as calculated members, and it will only be executed with certain members of other attributes Example: If we want to multiply the salaries of employees in united states only by 10, then we will create a custom member formula and set its value with united states only with salary*10.

Custom Member Vs Calculated Member Exists in dimension table It can be considered as a modification or altering to the aggregation formula of already existing members Calculated Member: It is a new virtual member not existing in the dimension table Considered as a dimension member.

Cell and Tuple Cell: Tuple: Certain value within a dimension or/and fact. Tuple: Certain row within a dimension or/and fact.

Set and Named Set Set: Named Set: It is a group of rows of data. It can be considered as a calculated set, also it can be considered as a “view” with certain conditions other than the normal behavior if the set.

Example on Named Set If you want to select employees whose salaries are more than 3000, and you will use this set of data in multiple queries, then you can put the output of this query in a named set where you can use in the multiple queries you want to use, and if there is a change in any condition of this set you will not have to change in each query, you will just have to change in the named set, and each query which uses this named set will be affected with the change.

KPI Measurement for measuring business success. Frequently evaluated over time and varies from one organization to the other. Consists of: Goal – [ Target ] Value – [ Actual ] Status – [ Score ] Trend – [ Upward, Downward] Status indicator Trend indicator Parent KPI Weight – [ In case of parent KPI ]

MDX Language for OLAP databases Stands for?

Dimension Types Standard Dimension Time Dimension Server Time Dimension

Dimension Relationship to Facts Regular: A regular dimension relationship between a cube dimension and a measure group (Fact) exists when the key column for the dimension is joined directly to the fact table. This direct relationship is based on a primary key–foreign key relationship in the underlying relational database, but might also be based on a logical relationship that is defined in the data source view

Dimension Relationship to Facts Cont’d Reference (Snowflake): A reference dimension relationship between a cube dimension and a measure group exists when the key column for the dimension is joined indirectly to the fact table through a key in another dimension table.

Dimension Relationship to Facts Cont’d Fact (Degenerate): It is a fact table that acts as a dimension for itself, rather than using a separate dimension table. Usually it is used for drill to details information.

Dimension Relationship to Facts Cont’d Many To Many: It is the same as the many to many relationship in the relational database, and we should overcome this by using an intermediate table.

Summary OLAP Data Warehouse Dimensions Facts Measure Members Dimension Relationship to Facts

Questions

THANK YOU… A VERY BIG THANK YOU TO Sherif Anwar 