Data modeling star schema
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The document provides information on skills needed to be a database professional. It lists logical data modeling, translating logical models into real database systems, special design challenges like security and access, normalization from 1NF to 5NF, and tools for data modeling like ER-Studio and ER-Win as important skills. It also discusses star schemas and snowflake schemas for data warehousing, with star schemas being better for performance in most cases.
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Data modeling star schema
- 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 [email protected]
- 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
- 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
- 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
- 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.
- 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.
- 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