Be a database professional

1. SKILLS TO BE A DATABASE PROFESSIONAL
 Sayed Ahmed
 Computer Engineering, BUET, Bangladesh
 MSC, Computer Science, U of Manitoba, Canada
 Software Engineer/Developer, Canada
 Owner/President/Architect/Developer
 Justetc (Just et cetera) Technologies
 http://www.justetc.net
 http://sayed.justetc.net
 sayed@justetc.net

2. NOTE
 Still under construction
 Will be updated later

3. MUST WATCH:PREREQUISITE
 In Bengali, Fundamentals of Database
Management Systems
 In English, Fundamentals of Database
Management Systems

4. LOGICAL DATA MODELING
 Logical Data Modeling: Logical Database Design Steps: RDBMS
 http://salearningschool.com/displayArticle.php?table=Articles&articleID=773
 Logical Data Modeling
 Identify major entities
 Det ermine relationships between entities
 Determine primary and alternate keys
 Determine foreign keys
 Determine key business rules
 Add remaining attributes
 Validate user views through normalization
 Determine domains
 Determine triggering operations
 Combine user views
 Integrate with existing data models
 Analyze for stability and growth

5. LOGICAL MODEL INTO THE REAL DATABASE SYSTEM IDENTIFY TABLES
 Translate Logical Model into the Real Database System
Identify tables
 Identify columns
 Adapt data structure to product environment
 Design for business rules about entities
 Design for business rules about relationships
 Design for additional business rules about attributes
 Tune for scan efficiency
 Define clustering sequences
 Define hash keys
 Add indexes
 Add duplicate data
 Redefine columns
 Redefine tables

6. SPECIAL DESIGN CHALLENGES
 Design for Special Design Challenges
 Provide for access through views
 Establish security
 Cope with very large databases
 Access and accommodate change
 Anticipate relational technology evolution

7. 3-NF NORMALIZATIONS
 http://en.wikipedia.org/wiki/Third_normal_for
m
 Boyce/Codd and Fourth Normal Form
 http://salearningschool.com/displayArticle.php?ta
ble=Articles&articleID=640
 Normalization in Relational DBMS
Systems
 http://salearningschool.com/displayArticle.php?ta
ble=Articles&articleID=639

8. NORMALIZATION (1NF TO 5TH NF)
 Normalization (1NF to 5th NF)
 http://salearningschool.com/displayArticle.php?ta
ble=Articles&articleID=600

9. MODELS
 Conceptual, logical, and Physical
 http://en.wikipedia.org/wiki/Logical_data_model

10. EXAMPLES OF DATA MODELS
 Must Watch: Understanding Models
 http://www.learndatamodeling.com/cdm.php#.Ui
KHVz_OCys

11. TOOLS THAT YOU SHOULD LEARN
 Tools that You Should Learn
 Just learn them
 If you are good with DBMS theories, they will
not be difficult, you can do it mostly on your
own

12. ER-STUDIO
 http://www.embarcadero.com/products/er-
studio
 http://en.wikipedia.org/wiki/ER/Studio

13. ER-STUDIO

14. ER/STUDIO DATA ARCHITECT
 Universal Mappings Map between and within
conceptual, logical and physical model objects to
view upstream or downstream "Where Used"
Analysis Display mapping between conceptual and
logical models and their implementations across
physical designs Visual Data Lineage Visually
document source/target mapping and sourcing rules
for data movement across systems Round-trip
Database Support Round-trip database support for
forward and reverse engineering Advanced Compare
and Merge Enable advanced, bidirectional
comparisons and merges of model and database
structures

15. ER/STUDIO PORTAL

16. ER/STUDIO PORTAL
 Structured Browsing & Navigation Provide a
web-based navigation of the repository
diagrams Technical Reports Pre-installed for
implementation details such as data types,
column width, column names, how objects are
related, data lineage between models and
security classification information Automatic
Data Synchronization ER/Studio diagrams and
objects are synchronized to the Portal on an
administrator controlled schedule. Advanced
Searching Wildcard searching with the ability to
limit the search to specific object types

17. ER/STUDIO REPOSITORY

18. ER/STUDIO REPOSITORY
 Concurrent Model and Object Access Allows real-time
collaboration between modelers working on data models
down to the model object level Reviewing Changes and
Resolving User Conflict Conflict resolution through simple
and intelligent interfaces to walk users through the
discovery of differences Version Management Manages
the individual histories of models and model objects to
ensure incremental comparison between, and rollback to,
desired diagrams Component Sharing and Reuse Pre-
defined Enterprise Data Dictionary that eliminates data
redundancy and enforces data element standards
Security Center Groups Streamline security
administration with local or LDAP groups improving
productivity and reducing errors

19. ER/STUDIO BUSINESS ARCHITECTS
 Skip this
 Conceptual Model Creation Supports high-
level conceptual modeling using elements
such as subject areas, business entities,
interactions, and relationships Process
Model Creation Support for straightforward
process modeling that uses standard
elements such as sequences, tasks, swim
lanes, start events, and gateways

20. ER/STUDIO SOFTWARE ARCHITECT
 Skip this
 Model Driven Architecture & Standards
Supports Unified Modeling
LanguageTM(UML® 2.0 ), XML Metadata
Interchange (XMI® ), Query/
Views/Transformations (QVT) and Object
Constraint Language (OCL) Model Patterns
Powerful re-use facilities to jumpstart
projects through predefined patterns.

21. ER-WIN
 http://en.wikipedia.org/wiki/CA_ERwin_Data_Modeler
 Logical Data Modeling: Purely logical models may be created, from which physical models may
be derived. Combinations of logical and physical models are also supported. Supports entity-
type and attribute logical names and descriptions, logical domains and data types, as well as
relationship naming.
 Physical Data Modeling: Purely physical models may be created as well as combinations of
logical and physical models. Supports the naming and description of tables and columns, user
defined data types, primary keys, foreign keys, alternative keys and the naming and definition of
constraints. Support for indexes, views, stored procedures and triggers is also included.
 Logical-to-Physical Transformation: Includes an abbreviation/naming dictionary called "Naming
Standards Editor" and a logical-to-RDBMS data type mapping facility called "Datatype Standards
Editor", both of which are customizable with entries and basic rule enforcement.
 Forward engineering: Once the database designer is satisfied with the physical model, the tool
can automatically generate a SQL Data Definition Language (DDL) script that can either be
directly executed on the RDBMS environment or saved to a file.
 Reverse engineering: If an analyst needs to examine and understand an existing data structure,
ERwin will depict the physical database objects in an ERwin model file.
 Model-to-model comparison: The "Complete/Compare" facility allows an analyst or designer to
view the differences between two model files (including real-time reverse-engineered files), for
instance to understand changes between two versions of a model.
 An "Undo" feature is available in version 7.

22. POWER-DESIGNER
 http://en.wikipedia.org/wiki/PowerDesigner
 PowerDesigner includes support for:
 Business Process Modeling (ProcessAnalyst) supporting BPMN
 Code generation (Java, C#, VB .NET, Hibernate, EJB3, NHibernate, JSF,
WinForm (.NET and .NET CF), PowerBuilder, ...)
 Data modeling (works with most major RDBMS systems)
 Data Warehouse Modeling (WarehouseArchitect)
 Eclipse plugin
 Object modeling (UML 2.0 diagrams)
 Report generation
 Supports Simul8 to add simulation functions to the BPM module to enhance
business processes design.
 Repository
 Requirements analysis
 XML Modeling supporting XML Schema and DTD standards
 Visual Studio 2005 / 2008 addin

23. DATAWAREHOUSE SCHEMAS
Datawarehouse Schemas

24. SNOWFLAKE SCHEMA VS STAR SCHEMA
 http://www.diffen.com/difference/Snowflake_
Schema_vs_Star_Schema

25. SNOWFLAKE SCHEMA VS STAR SCHEMA

26. SNOWFLAKE SCHEMA VS STAR SCHEMA

27. DATAWAREHOUSE VS OLTP
In School, you may study a bit on Datawarehouse
However, you may not learn that though there are very few opportunities but
the successful professional are highly paid

28. DATA WAREHOUSE
 http://salearningschool.com/searchResult.php?q
ueryStr=warehouse&submit=Search+Database
 How to implement BI/Warehouse
Overview on SAP CRM
Random Information on BI
Steps in Data Warehouse Design and
Implementation
What is Data Warehousing?

29. STAR AND SNOWFLAKE SCHEMAS
 http://www.oracle.com/webfolder/technetwork
/tutorials/obe/db/10g/r2/owb/owb10gr2_gs/o
wb/lesson3/starandsnowflake.htm
 Star and Snowflake Schemas
 In relational implementation, the dimensional
designs are mapped to a relational set of tables.
You can implement the design into following two
methods:
 Star Schema
 Snowflake Schema

30. STAR SCHEMA
 What Is a Star Schema?
 A star schema model can be depicted as a simple star: a
central table contains fact data and multiple tables radiate
out from it, connected by the primary and foreign keys of
the database. In a star schema implementation,
Warehouse Builder stores the dimension data in a single
table or view for all the dimension levels.
 For example, if you implement the Product dimension
using a star schema, Warehouse Builder uses a single
table to implement all the levels in the dimension, as
shown in the screenshot. The attributes in all the levels
are mapped to different columns in a single table called
PRODUCT.

31. EXAMPLE: STAR SCHEMA

32. WHAT IS A SNOWFLAKE SCHEMA?
 What Is a Snowflake Schema?
 The snowflake schema represents a dimensional
model which is also composed of a central fact table
and a set of constituent dimension tables which are
further normalized into sub-dimension tables. In a
snowflake schema implementation, Warehouse
Builder uses more than one table or view to store the
dimension data. Separate database tables or views
store data pertaining to each level in the dimension.
 The screenshot displays the snowflake
implementation of the Product dimension. Each level
in the dimension is mapped to a different table.

33. SNOW-FLAKE SCHEMA

34. WHEN TO USE STAR/SNOW-FLAKE SCHEMAS
Ralph Kimball recommends that in most of the other cases, star
schemas are a better solution. Although redundancy is reduced in
a normalized snowflake, more joins are required. Kimball usually
advises that it is not a good idea to expose end users to a physical
snowflake design, because it almost always compromises
understandability and performance.

35. WHEN DO YOU USE SNOWFLAKE SCHEMA IMPLEMENTATION?
 When do you use Snowflake Schema Implementation?
 Ralph Kimball, the data warehousing guru, proposes three cases where
snowflake implementation is not only acceptable but is also the key to a
successful design:
 Large customer dimensions where, for example, 80 percent of the fact table
measurements involve anonymous
visitors about whom you collect little detail, and 20 percent involve reliably
registered customers about
whom you collect much detailed data by tracking many dimensions

 Financial product dimensions for banks, brokerage houses, and insurance
companies, because each of
the individual products has a host of special attributes not shared by other
products

 Multienterprise calendar dimensions because each organization has
idiosyncratic fiscal periods,
seasons, and holidays

36. GOT QUESTIONS?
http://ask.justetc.net