1. Ahsan Abdullah 1 Data Warehousing Lecture-7De-normalization Virtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research www.nu.edu.pk/cairindex.asp National University of Computers & Emerging Sciences, Islamabad Email: ahsan@cluxing.com

2. Ahsan Abdullah 2 De-normalization

3. 3 Striking a balance between “good” & “evil” Flat Table Data Lists Data Cubes 1 st Normal Form 2 nd Normal Form 3 rd Normal Form 4+ Normal Forms Normalization De-normalization One big flat file Too many tables

4. Ahsan Abdullah 4 What is De-normalization?  It is not chaos, more like a “controlled crash” with the aim of performance enhancement without loss of information.  Normalization is a rule of thumb in DBMS, but in DSS ease of use is achieved by way of denormalization.  De-normalization comes in many flavors, such as combining tables, splitting tables, adding data etc., but all done very carefully.

5. Ahsan Abdullah 5 Why De-normalization In DSS?  Bringing “close” dispersed but related data items.  Query performance in DSS significantly dependent on physical data model.  Very early studies showed performance difference in orders of magnitude for different number de-normalized tables and rows per table.  The level of de-normalization should be carefully considered.

6. Ahsan Abdullah 6 How De-normalization improves performance? De-normalization specifically improves performance by either:  Reducing the number of tables and hence the reliance on joins, which consequently speeds up performance.  Reducing the number of joins required during query execution, or  Reducing the number of rows to be retrieved from the Primary Data Table.

7. Ahsan Abdullah 7 4 Guidelines for De-normalization 1. Carefully do a cost-benefit analysis (frequency of use, additional storage, join time). 2. Do a data requirement and storage analysis. 3. Weigh against the maintenance issue of the redundant data (triggers used). 4. When in doubt, don’t denormalize.

8. Ahsan Abdullah 8 Areas for Applying De-Normalization Techniques  Dealing with the abundance of star schemas.  Fast access of time series data for analysis.  Fast aggregate (sum, average etc.) results and complicated calculations.  Multidimensional analysis (e.g. geography) in a complex hierarchy.  Dealing with few updates but many join queries. De-normalization will ultimately affect the database size and query performance.

9. Ahsan Abdullah 9 Five principal De-normalization techniques 1. Collapsing Tables. - Two entities with a One-to-One relationship. - Two entities with a Many-to-Many relationship. 2. Splitting Tables (Horizontal/Vertical Splitting). 3. Pre-Joining. 4. Adding Redundant Columns (Reference Data). 5. Derived Attributes (Summary, Total, Balance etc).

10. Ahsan Abdullah 10 Collapsing Tables ColAColBColAColC normalized ColAColBColC denormalized  Reduced storage space.  Reduced update time.  Does not changes business view.  Reduced foreign keys.  Reduced indexing.