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Eğitim takvimi Workshop Takvimi
TDWI Advanced Data Modeling Techniques Yeni Dönem
This course explores different situations facing data modeling practitioners and provides information and techniques to help them develop the appropriate data models.
EĞİTİM DETAYLARI
Kalan Kontenjan 6 / 25
13 NİSAN 2024
Cumartesi - Pazar 10.00 / 16.00
2 Oturum
Online Eğitim

Uzaktan Eğitim

Canlı Sınıf

KAYIT VE ÖDEME DETAYLARI
Fiyat, ödeme koşulları, eğitim konularının detaylı bilgisi için form doldurun, arayarak bilgi verilecektir.

Tüm sorularınız için : 444 1 476 veya info@mindset.com.tr

DÖNEM
TARİH
GÜNLER
SÜRE
KONTENJAN
NAKİT FİYATI
KREDİ KARTI
Yeni Dönem
13.04.2024
Cumartesi - Pazar 10.00 / 16.00
12
25 / 6

TDWI Advanced Data Modeling Techniques Yeni Dönem

444 1 476
EĞİTİM İÇERİĞİ

Genel Bilgi

Whether you are a business data modeler who represents data requirements as entities and relationships, or a physical data modeler more concerned with tables, columns, and indexes, you know that the hard stuff lies beneath the surface. Every data design, whether logical or technical, is challenged by one or more complex considerations—scalability, adaptability, performance, legacy and package databases, and more. Every data model raises questions. Advanced modeling techniques provide many of the answers. This course explores different situations facing data modeling practitioners and provides information and techniques to help them develop the appropriate data models.

Genel Bilgi
Module 1: Data Modeling Concepts
  • Enterprise Architecture
    • Definition
    • Zachman Framework Overview
    • Data Modeling Framework for BI
    • Levels of Data Models – Enterprise Perspective
    • Levels of Data Models – Project Perspective
    • The Open Group Architecture Framework
    • Control Objectives for Information Technology
    • Frameworks – Discussion
  • Higher Normal Forms
    • Boyce-Codd Normal Form
    • Fourth Normal Form
    • Fifth Normal Form
    • Anchor Modeling
    • Data Vault Modeling
  • Specialization and Generalization
    • Roles and Classifications
    • Considerations
    • Party
  • Presentation
    • Standards
Module 2: Business Data Model Development
  • Business Data Model Development Approaches
    • Top-Down
    • Bottom-Up
    • Generic Models
    • Limited Depth Models
  • Data Modeling Roles
    • Functions, Traits, and Challenges
  • Business Data Model Application
    • Basis for System Data Model
    • Transformation and Integration Foundation
    • Package Selection
    • Business Communications
    • Data Profiling
    • Data Governance
  • Data Governance
    • Definition
    • Quality Improvement
    • Real-Time Implications
    • Metadata Management
    • Information Subject Area
    • Big Data
    • Big Data Challenges
Module 3: System and Physical Data Model Development
  • Data Modeling Roles
    • Functions, Traits, and Challenges
  • Globalization / Localization
    • Information Needs
    • Currencies
    • Time Zones
    • Languages
  • Non-Relational Data Structures
    • Columnar Databases
    • In-Memory Databases
    • XML Structures
    • Key Value Pairs
  • Business Analytics
    • Definition
    • Schema on Read
    • Modeling Process
Module 4: Additional Concepts
  • Recursive Relationships
    • Normalized Approach
    • Dimensional Approach
  • Cloud
    • Modeling Implications
  • Complementary Models
    • State Transition Model
    • Function Models
    • Process Models
    • Model Management
  • Model Management
    • Model Validation and Testing
    • Model Synchronization
    • Tool Exploitation
    • Data Modeling Tools
    • Repositories
Module 5: Summary and Conclusions
  • Summary of Key Points
    • A Quick Review
Appendix B: Exercises
  • Exercise 1: Normalization to Higher Normal Forms
  • Exercise 2: Party Modeling
  • Exercise 3: Financial Institution Model
  • Exercise 4: Model Application for Data Profiling
  • Exercise 5: Application System Model Development
  • Exercise 6: Model Evaluation
Genel Bilgi

Whether you are a business data modeler who represents data requirements as entities and relationships, or a physical data modeler more concerned with tables, columns, and indexes, you know that the hard stuff lies beneath the surface. Every data design, whether logical or technical, is challenged by one or more complex considerations—scalability, adaptability, performance, legacy and package databases, and more. Every data model raises questions. Advanced modeling techniques provide many of the answers. This course explores different situations facing data modeling practitioners and provides information and techniques to help them develop the appropriate data models.

Module 1: Data Modeling Concepts
  • Enterprise Architecture
    • Definition
    • Zachman Framework Overview
    • Data Modeling Framework for BI
    • Levels of Data Models – Enterprise Perspective
    • Levels of Data Models – Project Perspective
    • The Open Group Architecture Framework
    • Control Objectives for Information Technology
    • Frameworks – Discussion
  • Higher Normal Forms
    • Boyce-Codd Normal Form
    • Fourth Normal Form
    • Fifth Normal Form
    • Anchor Modeling
    • Data Vault Modeling
  • Specialization and Generalization
    • Roles and Classifications
    • Considerations
    • Party
  • Presentation
    • Standards
Module 2: Business Data Model Development
  • Business Data Model Development Approaches
    • Top-Down
    • Bottom-Up
    • Generic Models
    • Limited Depth Models
  • Data Modeling Roles
    • Functions, Traits, and Challenges
  • Business Data Model Application
    • Basis for System Data Model
    • Transformation and Integration Foundation
    • Package Selection
    • Business Communications
    • Data Profiling
    • Data Governance
  • Data Governance
    • Definition
    • Quality Improvement
    • Real-Time Implications
    • Metadata Management
    • Information Subject Area
    • Big Data
    • Big Data Challenges
Module 3: System and Physical Data Model Development
  • Data Modeling Roles
    • Functions, Traits, and Challenges
  • Globalization / Localization
    • Information Needs
    • Currencies
    • Time Zones
    • Languages
  • Non-Relational Data Structures
    • Columnar Databases
    • In-Memory Databases
    • XML Structures
    • Key Value Pairs
  • Business Analytics
    • Definition
    • Schema on Read
    • Modeling Process
Module 4: Additional Concepts
  • Recursive Relationships
    • Normalized Approach
    • Dimensional Approach
  • Cloud
    • Modeling Implications
  • Complementary Models
    • State Transition Model
    • Function Models
    • Process Models
    • Model Management
  • Model Management
    • Model Validation and Testing
    • Model Synchronization
    • Tool Exploitation
    • Data Modeling Tools
    • Repositories
Module 5: Summary and Conclusions
  • Summary of Key Points
    • A Quick Review
Appendix A: Bibliography and References
Appendix B: Exercises
  • Exercise 1: Normalization to Higher Normal Forms
  • Exercise 2: Party Modeling
  • Exercise 3: Financial Institution Model
  • Exercise 4: Model Application for Data Profiling
  • Exercise 5: Application System Model Development
  • Exercise 6: Model Evaluation
PROGRAM HEDEFİ

PROGRAM HEDEFİ

  • Enterprise architecture approaches and how to apply them
  • How big data and analytics impact traditional approaches
  • Different data models and how they relate to each other
  • The role of modeling in analytics
  • Higher normalization forms
  • How to effectively apply generalization and specialization
  • The role of metadata management in data governance
  • State and time dependencies and how to handle them
  • How to validate the data model
  • How to transform the business data model into physical models based on the application
  • The implications of alternative storage approaches
  • The roles and structures of complementary models
  • How to deal with multiple time zones and currencies
KAYIT ve ÖDEME

KAYIT ve ÖDEME

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Ödeme Seçenekleri:

Kredi Kartıyla Peşin Ödeme

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EFT / Havale ile ödeme.

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