Data Warehouses and Dimensional Modelling

Question 1

Main goal of DW and Dimensional Modelling

Answer 1

Simplicity.

Ensures that users can easily understand databases.

Allows software to efficiently navigate databases.

Question 2

Dimensional Modelling (DM) Definition

Answer 2

A logical design technique for structuring data so that it’s intuitive to business users and delivers fast query performance.

Question 3

Three primary design goals

Answer 3

1. Present the required information to users as simply as possible (understandability).
2. Return query results to the users as quickly as possible (query performance)
3. Provide relevant information that accurately tracks the underlying business processes

Question 4

Present the required information to users as simply as possible (understandability).

Answer 4

1. Make everything as simple as possible, but not simpler. (Einstein)
2. DMs are much easier for users to understand - information is grouped into coherent business categories.
3. DMs typically contain exactly the same content as a normalized model, but with far fewer tables.
4. Simplicity is relative - the model must reflect complex business processes.

Question 5

Return query results to the users as quickly as possible (query performance)

Answer 5

1. Relational environment:
   - Help query performance because of denormalization.
   - Pre-join hierarchies and lookup tables - fewer join paths and less immediate temporary tables.
   - Predictable framework allows the DBMS to make strong assumptions which aid in performance.
2. OLAP environment:
   - Engine designed to support DMs
   - Performance due to aggregation within and across dimensions.

Question 6

Provide relevant information that accurately tracks the underlying business processes

Answer 6

Requires full range of design patterns to create models that accurately capture and track the business.

Question 7

Benefits of Dimensional Modelling

Answer 7

1. Understandability
2. Query performance
3. Each dimension is an equivalent entry point into the fact table. Therefore, query performance is simple, predictable and controllable.
4. Graceful extensibility to accommodate new data. No query or BI application needs to be reprogrammed to accommodate changes.

Question 8

Star Join

Answer 8

Each business process can be represented by a DM that consists of a normalized fact table surrounded by denormalized dimension tables.

Question 9

Fact table

Answer 9

1. Highly normalized, storing little redundant data.
2. Contains the measurements associated with a specific business process.
3. Record in fact table = measurement of a business event.
4. Primary key is a multi-part/composite key made up of a subset of foreign keys from business process dimensions.
   - Fact tables always express many to many relationships between dimensions.
   - Every foreign key must match a unique primary key in the corresponding dimension.
5. Some business processes track events that do not contain facts. Then a factless fact table is created. Add a dummy counter variable with value 1.

Question 10

Facts

Answer 10

Numeric values (typically continuous) that quantify the magnitude of the event.

Question 11

Three types of facts

Answer 11

1. Numeric and additive
   - Can meaningfully be aggregated across dimensions.
   - Most useful facts.
2. Semi-additive
   - Can be meaningfully aggregated across some dimensions.
   - Can typically not be aggregated across time dimensions.
3. Non-additive
   - Cannot be meaningfully aggregated across any dimensions.
   - Stored in dimension tables

Question 12

When do facts conform?

Answer 12

If their definitions are the same in different fact tables

Question 13

What is the grain of a fact table?

Answer 13

The level of detail contained in a fact table.

Question 14

Lowest level of detail that is possible

Answer 14

Atomic level.

Complete flexibility to roll up to any summary level.

Question 15

Three fundamental grains

Answer 15

1. Transaction fact table
   - Track each transaction as it occurs at a point in time
   - One record per transaction/line of transaction
2. Periodic fact table
   - Captures cumulative performance over specific time intervals.
   - One record per group of transactions made over a period of time.
   - Complements detailed transaction facts.
3. Accumulating snapshot fact table
   - Constantly updated over time
   - One record for the entire lifetime of an event.
   - Used to combine data across several business processes in a value chain.

Question 16

Dimension Tables

Answer 16

1. The nouns of the dimensional model.
2. Describes the objects that participate in the business.
3. Spot dimensions and/or their attributes in conversations.
4. Provide entry points into the data.
5. Has single surrogate primary key.
6. Conformed dimension: single dimension that is shared by two or more business processes.
7. Embedded hierarchy: some dimension attributes relate to each other in a hierarchical or 1:M fashion.
8. Relational transaction systems designed using normalization.
9. Each row has a set of attributes that provides context which is true the moment the fact table is recorded.
10. Dimension attributes are typically textual fields.

Question 17

What is denormalization?

Answer 17

Process of recombining the attributes into a single dimension.

Nothing is lost. It presents exactly the same information.

Question 18

What are conformed dimensions?

Answer 18

Two dimensions are conformed if they contain one or more fields with the same names and contents.

Two types:

1. Identical conformed dimensions.
2. One dimension is a perfect subset of a more detailed granular dimension table.

Question 19

Benefits of conformed dimensions

Answer 19

1. Consistency
   - Ensure that every fact table is filtered consistently and the resulting query answer sets are labeled consistently.
2. Integration
   - Allows queries to drill across fact tables representing different processes (supports true cross-business process analysis)
3. Reduced development time to market
   - Do not need to recreate the wheel over and over again.

Question 20

What is a Bus Matrix?

Answer 20

It is a representation of conformed dimensions’ involvement/participation in multiple business processes.

Each row represents a business process and defines at least one fact table and its associated dimensions.

Question 21

What do you call it when one business process has more than one DM

Answer 21

Business process dimensional model

Question 22

What does it mean to “Drill Across”?

Answer 22

Analysis involving data from more than one business process.

Question 23

What are Surrogate Keys?

Answer 23

1. New set of keys, separate from keys in source system.
2. Also known as meaningless keys, substitute keys, non-natural keys or artificial keys.
3. Unique value, usually an integer, assigned to each row in a dimension.
4. Becomes a primary key of dimension table used to join with associated foreign key in fact table.

Question 24

Benefits of surrogate keys

Answer 24

1. Helps protect DW from unexpected administrative changes in source system keys (called business keys or natural keys)
2. Allow the DW to integrate the same data with different keys from multiple source systems.
3. Enable you to add rows to dimensions that do not exist in the source system.
4. Provide the means for tracking changes in dimension attributes over time.
5. Integer surrogate keys can improve query and processing performance.

Question 25

What is snowflaking?

Answer 25

Connecting lookup tables in fields of dimension tables.

Question 26

Why is snowflaking a discouraged technique?

Answer 26

It makes the model more complex, less usable and more difficult to maintain.

Question 27

What is an outrigger table?

Answer 27

It is a purpose-specific snowflake table. Typical reasons for creating outrigger table: 1. Rarely used lookups 2. Large number of attributes 3. Different grain 4. Different update frequency

Question 28

What are slowly changing dimensions?

Answer 28

Techniques used to manage attribute changes over time. Attributes are not fixed, can change over time (though relatively infrequently compared to facts). Ability to track changes over time is one of the fundamental reasons for the existence of DWs.

Question 29

Four basic types of slowly changing dimensions

Answer 29

Type 1: - Changes business doesn't care about - Overwrite existing attribute value with new value Type 2: - Create new row in dimension table to capture new value(s) - New surrogate key and data stamps to show when the row came into effect and when it expires. - Existing row is marked to show that its attributes were in effect right up until the creation of the new row. - All fact rows going forward will be assigned the new surrogate key. - Existing fact rows keep old surrogate key. Type 3: - Keeps separate columns for old and new attribute values (called alternative realities) - Can lead to very wide table. Type 4: - Separate more frequently changing/used attributes into their own separate dimension table, a.k.a. mini-dimension. - Create surrogate primary key for mini-dimension with corresponding foreign key in fact table, distinct from the surrogate key of the base dimension.

Question 30

Describe Dates

Answer 30

Fundamental business dimension across all organizations and industries. One row for every day for which you expect data in the fact table. Grain is at a day level rather than time of day. It is a role playing dimension. Use surrogate key for date. This helps to manage the problems of missing dates.

Question 31

What are degenerate dimensions?

Answer 31

Transaction identifiers often end up as degenerate dimensions without joining to an actual dimension table. It does not exist outside the transaction and has no associated descriptive attributes that aren't already in dimensions. Include in fact table because it serves an analytical purpose - tie together all line items in a market basket.

Question 32

What are Hierarchies?

Answer 32

Meaningful, standard ways to group data within a dimension. Possibly many hierarchies in one dimension. Allows drill down - begin with big picture and "drill down" to lower levels in the hierarchy. Main paths for summarizing the data. Flattening hierarchies is one of the main design tasks of creating a dimensional table.

Question 33

What are junk dimensions?

Answer 33

Combination of columns of the separate small dimensions into a single table. One row for each combination of values. One fact row joins to one dimension row.

Question 34

Two ways of aggregating dimensions

Answer 34

1. Remove dimension entirely | 2. Roll up in a dimension's hierarchy.