This document discusses logical design of data warehouse fact tables. It covers defining fact table column types including measures, foreign keys and surrogate keys. It describes additive, semi-additive and non-additive measures and how they impact aggregation. Finally, it discusses resolving many-to-many relationships in a star schema by creating an intermediate dimension table.
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Fact table design for data ware house
1. SOFTWARE/WEB/MOBILE/DATABASE ARCHITECT, ENGINEER, AND DEVELOPER
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Data Warehouse Fact Table Design
Logical Design of a Data Warehouse
2. This is a video just for the sake of a video
I will not explain anything
Just will walk through on some presentation
slides on Data Warehouse
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4. FACT TABLE DESIGN
Fact Table Design Topics
Define fact table column types
Understand the additivity of a measure
i.e. for measure column type
Handle many-to-many relationships in a Star
schema
6. FACT TABLE COLUMNS - MEASURE
Measure Column Type
Measure columns help with measurements
useful for a specific business process
Measures columns are usually numeric
And can be aggregated
Measure columns store values that are of
interest to businesses such as
sales amount, order quantity, and discount amount
7. MEASURES
Fact tables - Measures
Collection of measurements on a specific
aspects of business
Measure columns
sales amount, order quantity, and discount
amount.
8. FACT TABLE COLUMNS – FOREIGN KEY
Foreign Key – Column Type
These are the columns as coming from
Dimension Tables
Are the primary keys of Dimension Tables
9. FACT TABLE COLUMNS – SURROGATE KEYS
Surrogate Key
Usually will comes from the primary dimension
table (primary for the current fact table)
Usually one or two columns in a fact table are
surrogate keys
10. SURROGATE KEYS FOR FACT TABLES
OrderId and LineItemId are the
surrogate keys as coming from the
primary Source Order details table
OrderId and LineItemId columns will help
For quick comparisons with source data
Surrogate keys are not a must in fact tables;
however, they help
Must read:
http://www.kimballgroup.com/2006/07/d
esign-tip-81-fact-table-surrogate-key/
11. LINEAGE COLUMNS IN FACT TABLES
Lineage columns
Just as with dimension tables, these are strictly
for auditing purposes
References:
https://upsearch.com/implementing-a-data-
warehouse-fact-tables/
12. ADDITIVITY OF MEASURES
The primary purpose of Data warehouse is reporting,
and forecasting
Many times reports are aggregations such as sum
Example: sales by quarter, by region, by product type
Many reports are usually aggregations
Hence, fact tables will have some columns to assist
with that measures and aggregations for reporting
These are the measures columns as we discussed
before
The measures that you add will help in how you want
to do the measures and reporting
13. TYPES OF ADDITIVITY OF MEASURES
Types of Additivity of Measures
additive measures
Semi-additive measures
non-additive measures
14. ADDITIVE, SEMI-ADDITIVE MEASURES
Additive
If a measure can be summed across all dimensions
Semi-additive
Sometimes, we can sum a measure across all
dimensions except for time
such as account balance
We can’t sum the account balance across the time
dimension
We would need to do something like take the average
instead
or simply use the last value
Measures like this are called semi-additive measures
15. NON-ADDITIVE MEASURES
Some measures can’t ever be summed
These are called non-additive measures
Such as discount percentages and prices
These are the non-additive measures
16. ADDITIVITY OF MEASURES IN SSAS
SSAS has support for semi-additive and non-
additive measures
The SSAS database model
Business Intelligence Semantic Model (BISM).
BISM includes way more additional metadata
than RDBMS
SSAS has two types of storage
dimensional and
tabular
17. SSAS AND ADDITIVITY OF MEASURES
Tabular storage works through tables like a
data warehouse does
quicker to develop
The dimensional model
more properly represents a cube
The dimensional model includes
even more metadata than the tabular model
18. SSAS AND ADDITIVITY OF MEASURES
In BISM dimensional processing
SSAS offers semi-additive aggregate functions
out of the box
For example,
SSAS offers the LastNonEmpty aggregate
function
which properly uses the SUM aggregate function
across all dimensions but time
and defines the last known value as the aggregate
over time
19. SSAS AND ADDITIVITY OF MEASURES
In the BISM tabular model
you use the Data Analysis Expression (DAX)
language
The DAX language includes functions that let
you build semi-additive expressions quite quickly
as well
20. MANY TO MANY RELATIONSHIPS IN DATA WAREHOUSE
RDBMS Many to Many Relationship
21. MANY TO MANY IN RDBMS AND DW
RDBMS Model as above image is ok, Because
Every internet sales will have multiple reasons
Every reasons will have multiple internet sales
However, data warehouse has issues with such
modeling
because when using tools to build reporting the fact
table is expected to be on the many side
In the above picture, fact table is on the one side
In SSAS reporting, many times, we use tools that
build MDX queries that expect the schema to be
perfectly in star or snow-flake schema
22. HOW CAN WE RESOLVE ...
In SSAS with a BISM dimensional model
you can solve the problem by creating an
intermediate dimension between both fact tables
Picture as below
24. DW MANY TO MANY
You create it from the primary key of
theFactInternetSales table
DimFactInternetSales.
put it on the “one” side of the relationships with both fact
tables
Now, both fact tables are always on the “many”
side of any relationship
However, the relationship between
theFactInternetSales and the
new DimFactInternetSales dimension
is de facto one to one
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