Data ware house design

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Sayed Ahmed
Logical Design of a Data Warehouse

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3. TOPICS - KEYWORDS
 Design a Data Warehouse
 Star Schema
 Snow Flake Schema
 Dimension Tables
 Fact Tables
 Auditing
 Surrogate Keys
 Type 1, Type 2, Type 3, and Mixed solutions for
slowly changing dimension data ( SCD
management)
 Pivoting for Analysis
 To help with SSAS on data warehouse

4. TOPICS - KEYWORDS
 Design a Data Warehouse
 Additive measures
 Semi additive measures
 Hierarchies for dimensions
 Attributes in dimensions
 Attributes in lookup tables
 Long term data warehouse design
 Usually Star Schema
 Short term data warehouse design
 POC
 Usually snowflake schema

5. TOPICS - KEYWORDS
 Fact Tables
 measures
 foreign keys
 and possibly an additional primary key
 and lineage columns
 granularity of fact tables
 auditing and lineage needs
 Measures can be
 additive
 non-additive
 semi-additive

6. TOPICS - KEYWORDS
 dimension
 keys
 names
 attributes
 member properties
 translations
 and lineage

7. TOPICS - KEYWORDS
 attributes
 natural hierarchies
 many-to-many fact table relationships
 you can introduce an additional intermediate
dimension

8. CONCLUSION
 Not much – right
 However, if you understand all the terms and
can implement all these concepts in your data
warehouse
 That will be great
 Not necessarily you will need to use all of these
concepts; however, you may need to justify based
on the situation, will all or any of these will help?
 What will help and what will not help
 Check our sub sequent videos and tutorials

9. THANK YOU
 Any Concerns?
 http://ask.justetc.net
 Or comment below...

10. TOOLS AND SOFTWARE REQUIREMENTS
 Download the Adventure Works databases
 OLTP database (LOB database)
 Data warehouse Database
 From
 http://msftdbprodsamples.codeplex.com/releases/view/55330
 For this tutorial, you can just check our slides
 Though the following tools will help
 And probably check the details in the downloaded
databases esp. The AdventureWorksDW2012
 You will need help from SQL Server and SQL Server
MGMT Studio Tools

11. REQUIRED TOOLS
 Useful/Required SQL Server Components
 Database Engine Services
 Documentation Components
 Management Tools - Basic
 Management Tools – Complete
 SQL Server Data Tools

12. DATA WAREHOUSE DESIGN – THE DETAILS
 Data Warehouse Logical Design
 Topics: Design and Implement a Data Warehouse
 Design and implement dimensions.
 Design and implement fact tables
 Design Auditing
 track the source and time for data coming into a DW through
auditing i.e lineage information
 Why a Data Warehouse?
 It is hard to
 generate reports from OLTP/LOB/Transactional database
 To do Analysis on OLTP database data (some times)
 Get useful information/useful summarized and details data
to be used to take business decisions

13. DATA WAREHOUSE DESIGN – THE DETAILS
 Why a Data Warehouse?
 Data in OLTP are heavily normalized. The goal was
to keep one data only in one single place to reduce
redundancy and consistency of data
 You may end up with many tables 100s, 1000s
 To generate reports you may need to join many
tables – will be slow
 Historical data may not be there
 Data quality is also an issue
 For reporting or analyzing, you may need data from
multiple databases across many departments

14. WHY A DATA WAREHOUSE?
 So you can create a Data Warehouse
 By cleaning data
 With historical data
 Combining data from multiple sources
 Denormalizing data
 Using specific design geared towards Data
Warehouse design
 Some or many consider DW design is less complex than
relational database design
 Though it also has some complex areas to address... (by those
some or many)

15. SO WHAT DOES A DATA WAREHOUSE CONTAIN?
 Usually two schemas are used for a DW
 Star Schema-> looks like a star
 Snow Flake Schema
 Another one called Dimensional Model
 Includes both Star and Snow Flake in the same
Data Warehouse
 Both Schemas has tables of two types
 Dimension Tables
 Fact Tables

16. SO WHAT DOES A DATA WAREHOUSE CONTAIN?
 Fact Tables are in the center
 A Fact table joins/combines all the data required for
this reporting or for the business aspect of this
reporting
 Usually combines the primary keys of different tables that
contain data for this report/business aspect
 Dimension tables are all the other tables that
contain actual data
 Dimension tables are the tables that contain data
 these can be the actual tables in the OLTP database
without any modification (Snow Flake)
 Or Dimension tables can be newly created by
denormalizing the existing OLTP databases (Star)

17. SO WHAT DOES A DATA WAREHOUSE CONTAIN?
 So, you know now what are dimension tables
and what are fact tables
 Fact tables contain primary keys of all related tables
(here they are foreign keys)
 Dimension tables contain data
 Usually, it’s better that you keep your data
warehouse separate from your OLTP database
 So bring all the tables (dimension) here
 Or denormalize them and bring them here in the new
database

18. SIMPLIFIED: WHAT ARE STAR AND SNOWFLAKE SCHEMAS
 If you just create Fact tables and take all the
related tables from your OLTP/LOB databases
 You get a Snow Flake Schema
 Here all Dimension tables are still normalized (as
you just took them from the actual database)
 This is easy –
 so good for short-term, quick, and experimental Data
Warehouse
 One note, your reporting and analysis services
queries (MDX, DMV) will be slow with Snow Flake
Schemas

19. SO WHAT DOES A DATA WAREHOUSE CONTAIN?
 Now, when you denormalize the dimension
tables
 You get the start schema
 The Fact tables remain the same for example
 Star Schema is kind of standard and used a
lot
 Originally was developed in 1980’s

20. EXAMPLES: WHY REPORTING IN OLTP DATABASE IS NOT A GREAT IDEA
Sales amount for internet sales by different countries and historical years

21. WHY REPORTING IN OLTP DATABASE IS NOT A GREAT IDEA
 issues that I did not mention before
 If your OLTP database was well designed (?)
 It may be hard to find the tables related to the
reporting
 The table names and the column names can be tricky
– do not follow any conventions – do not have
meaning
 So it can be hard to find data for the reporting

22. WHY REPORTING IN OLTP DATABASE IS NOT A GREAT IDEA
 Note: Reality:
 The OLTP may not even be well designed (that makes
reporting hard sometimes) even the relationships as well
as normalization
 – here we assumed that OLTP is perfect
 In a long back project
 I had to re-write/verify/check/change/optimize/had to deal with
(whatever you say) 100s (not really 100s, can be close to 100) of
queries for a reporting system
 Had to change the interface from one button for one report
(easy to get lost)
 Into a drop down list of reports
 The relations among data were arbitrary – actually had only in the
mind of the designer – did not follow any standards – No ER – no
standard concepts---
 So it was a hard job..
 Anyway..

23. WHY REPORTING IN OLTP DATABASE IS NOT A GREAT IDEA
 In such cases
 Tools such as SQL Profiler might help
 you could create a test environment,
 try to insert some data through an LOB application
 have SQL Profiler identify where the data was inserted
 Another, issue with this particular example
 No lookup for dates and years
 You need to extract
 The tables may not contain even historical data
 No date field
 So no historical data

24. WHY REPORTING IN OLTP DATABASE IS NOT A GREAT IDEA
 If sales data reside in multiple databases even by
multiple departments
 How do you merge
 Identify and match
 Customer data can be in different database with no
common identification
 Data quality can be low
 Data missing
 Partial data
 Inconsistent data in multiple databases
 Data can be represented differenlt in different database
 M or F for gender
 1 or 0 for gender

25. STAR SCHEMA/FACT/DIMENSION/CUBE

26. TOTAL DW: MULTIPLE STAR SCHEMAS
 You saw one Star Schema for Internet Sales
 You can see another for Offline Sales
 Another for Accounting
 Your DW has many such Star Schemas
 And these start schemas need to be connected/related
 They will be connected when you use the same
dimensions for them
 i.e. If two star schemas have the same dimension they can
share that dimension
 Called: shared or conformed dimensions
 For SSAS, you can use shared dimensions only
 There is a concept of private dimension
 Not a great idea in practical and real life applications
 You cannot connect/compare/verify the data over the shared dimension

27. SHARED/CONFORMED DIMENSIONS

28. DENORMALIZED DIMDATE TABLE

29. SNOW FLAKES WILL BE MORE AND MORE NORMALIZED
 Everything can be normalized
 Or the first level can be normalized others
are not

30. NORMALIZED PRODUCT DIMENSION
In the Star Schema, you could use these normalized product table to get snow
flake schema (partially.) Could use all normalized dimensions to get full snow
flake

31. SNOW FLAKE
 In Snow flake, you may see partial than full
snow flakes in reality
 Though, in reality, better to go for star
schema
 Queries will be faster

32. PARTIAL SNOW FLAKE

33. GRANULARITY
 The number of Dimension Tables connected
to a fact table
 Dimension of a star schema
 Cube = 3 dimension
 SSAS operates/analyzes on Cube

34. AUDITING AND LINEAGE
 I will be very short on this
 In data warehouse, you may want some
auditing tables
 For every update, you should audit
 who made the update,
 when it was made,
 and how many rows were transferred
 to each dimension and
 fact table
 in your DW

35. AUDITING AND LINEAGE
 You will need additional fields/columns in
your dimension and fact tables to track
 When, and who, and from where the row data
was/were updated
 Your ETL process needs to be updated
 If you used SSIS for the ETL
 Modify SSIS packages so that you can record these
information

36. THANK YOU
 Any Concerns?
 http://ask.justetc.net
 Or comment below...

37. DESIGNING DIMENSIONS
 Keys . Used to identify entities
 Name columns . Used for human names of
entities
 Attributes . Used for pivoting in analyses
 Member properties . Used for labels in a
report
 Lineage columns . Used for auditing, and
never exposed to end users

38.  For analysis
 Pivot Table
 Pivot Graph
 For Dimensions
 The fields used as for pivoting are called
 Attributes
 Not all columns are attributes
 Attributes: based on what analysis are done
 In previous, slide you saw the different types of
columns

39.  Attributes
 For pivoting, discrete attributes with a small
number of distinct values is most appropriate
 Should not be continuous
 Keys are not good candidates for pivoting and
analysis
 To make continous column for pivoting
 Concert/utilize it as a small set of discrete values

40.  SSAS can discretize continuous attributes.
 Not always great – need business perspecyive as
well
 Age and Income are not good candidates for auto
discretize
 Naming columns to identify the entity
 Not good for pivoting or keys
 Address such as
 Columns used in reports as labels only, not for
pivoting, are called member properties.
 Can include translations

41.  Lineage and auditing columns
 Used for auditing data
 Never exposed to the users

43.  Possible Attributes
 BirthDate (after calculating age and discretizing the age)
 MaritalStatus
 Gender
 YearlyIncome (after discretizing)
 TotalChildren
 NumberChildrenAtHome
 EnglishEducation (other education columns are for translations)
 EnglishOccupation (other occupation columns are for
translations)
 HouseOwnerFlag
 NumberCarsOwned
 CommuteDistance

45.  FullDateAlternateKey (denotes a date in date
format)
 EnglishMonthName
 CalendarQuarter
 CalendarSemester
 CalendarYear
 Drill Down attributes
 CalendarYear →CalendarSemester → CalendarQu
arter → EnglishMonthName → FullDateAlternateKey
.

46.  why dimension columns used in reports for
labels are called member properties.
 In a Snowflake schema, lookup tables show you
levels of hierarchies. In a Star schema, you
need to extract natural hierarchies from the
names and content of columns. Nevertheless,
because drilling down through natural
hierarchies is so useful and welcomed by end
users, you should use them as much as
possible.

47. SLOWLY CHANGING DIMENSIONS
 Type 1
 History lost
 Type 2
 Keeps all history
 Type 3
 Keeps partial history
 You can use a combination
 For some columns type1 for others type 2

52. DESIGNING FACT TABLES
 Fact tables include measures, foreign keys,
and possibly an additional primary key and
lineage columns.
 Measures can be additive, non-additive, or
semi-additive.
 For many-to-many relationships, you can
introduce an additional intermediate
dimension.

53.  Fact tables
 Collection of measurements on a specific
aspects of business
 Measure columns
 sales amount, order quantity, and discount
amount.