1. Sayed Ahmed Logical Design of a Data Warehouse

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3.  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.  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.  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.  dimension  keys  names  attributes  member properties  translations  and lineage

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

8.  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

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

12.  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.  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.  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.  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.  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, 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.  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.  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. Sales amount for internet sales by different countries and historical years

21.  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.  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.  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.  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

26.  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

29.  Everything can be normalized  Or the first level can be normalized others are not

30. 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.  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

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

34.  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.  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

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