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

NOTE Still under construction Will be updated later

MUST WATCH:PREREQUISITE In Bengali, Fundamentals of Database

Management Systems In English, Fundamentals of Database

Management Systems

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

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

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

3-NF NORMALIZATIONS http://en.wikipedia.org/wiki/Third_norm

al_form Boyce/Codd and Fourth Normal

Form http://salearningschool.com/displayArticle.

php?table=Articles&articleID=640 Normalization in Relational DBMS

Systems http://salearningschool.com/displayArticle.

php?table=Articles&articleID=639

NORMALIZATION (1NF TO 5TH NF)

Normalization (1NF to 5th NF) http://salearningschool.com/displayArticle.

php?table=Articles&articleID=600

MODELS Conceptual, logical, and Physical

http://en.wikipedia.org/wiki/Logical_data_model

EXAMPLES OF DATA MODELS Must Watch: Understanding Models

http://www.learndatamodeling.com/cdm.php#.UiKHVz_OCys

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

ER-STUDIO http://www.embarcadero.com/products/

er-studio http://en.wikipedia.org/wiki/ER/Studio

ER-STUDIO

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

ER/STUDIO PORTAL

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

ER/STUDIO REPOSITORY

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

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

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.

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.

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

DATAWAREHOUSE SCHEMAS

Datawarehouse Schemas

SNOWFLAKE SCHEMA VS STAR SCHEMA

http://www.diffen.com/difference/Snowflake_Schema_vs_Star_Schema

SNOWFLAKE SCHEMA VS STAR SCHEMA

SNOWFLAKE SCHEMA VS STAR SCHEMA

DATAWAREHOUSE VS OLTPIn School, you may study a bit on DatawarehouseHowever, you may not learn that though there are very few opportunities but the successful professional are highly paid

DATA WAREHOUSE http://

salearningschool.com/searchResult.php?queryStr=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?

STAR AND SNOWFLAKE SCHEMAS

http://www.oracle.com/webfolder/technetwork/tutorials/obe/db/10g/r2/owb/owb10gr2_gs/owb/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

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.

EXAMPLE: STAR SCHEMA

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.

SNOW-FLAKE SCHEMA

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.

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 anonymousvisitors about whom you collect little detail, and 20 percent involve reliably registered customers aboutwhom you collect much detailed data by tracking many dimensions

  Financial product dimensions for banks, brokerage houses, and insurance

companies, because each ofthe 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

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