Skills to Be a database Professional

1. 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 Skills to Be a database Professional

2. Still under construction Will be updated later Note

3. In Bengali, Fundamentals of Database Management Systems In English, Fundamentals of Database Management Systems Must Watch:prerequisite

4. 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 Data Modeling

5. 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 Logical Model into the Real Database System Identify tables

6. 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 Special Design Challenges

7. http://en.wikipedia.org/wiki/Third_normal_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 3-NF Normalizations

8. Normalization (1NF to 5th NF) • http://salearningschool.com/displayArticle.php?table=Articles&articleID=600 Normalization (1NF to 5th NF)

9. Conceptual, logical, and Physical • http://en.wikipedia.org/wiki/Logical_data_model Models

10. Must Watch: Understanding Models • http://www.learndatamodeling.com/cdm.php#.UiKHVz_OCys Examples of Data Models

11. 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 Tools that You Should Learn

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

13. ER-Studio

14. 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 Data Architect

15. ER/Studio Portal

16. 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 Portal

17. ER/Studio Repository

18. 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 ConflictConflict 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 Repository

19. 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 Business Architects

20. 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/Studio Software Architect

21. 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 SQLData 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. ER-Win

22. 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) Eclipseplugin Object modeling (UML 2.0 diagrams) Report generation Supports Simul8 to add simulationfunctions 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 Power-Designer

23. Datawarehouse Schemas Datawarehouse Schemas

24. http://www.diffen.com/difference/Snowflake_Schema_vs_Star_Schemahttp://www.diffen.com/difference/Snowflake_Schema_vs_Star_Schema Snowflake Schema vs Star Schema

25. Snowflake Schema vs Star Schema

26. Snowflake Schema vs Star Schema

27. Datawarehousevsoltp 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. http://salearningschool.com/searchResult.php?queryStr=warehouse&submit=Search+Databasehttp://salearningschool.com/searchResult.php?queryStr=warehouse&submit=Search+Database • How to implement BI/WarehouseOverview on SAP CRMRandom Information on BISteps in Data Warehouse Design and ImplementationWhat is Data Warehousing? Data warehouse

29. http://www.oracle.com/webfolder/technetwork/tutorials/obe/db/10g/r2/owb/owb10gr2_gs/owb/lesson3/starandsnowflake.htmhttp://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 and Snowflake Schemas

30. 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. Star Schema

31. Example: Star Schema

32. 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. What Is a Snowflake Schema?

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? 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 When do you use Snowflake Schema Implementation?

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