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Data Architecture Foundations

At the end of this training, participants will be able to describe what data architecture is, what is involved in enterprise level data architecture practices, and what the core data architecture...

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$1,460 USD
Course Code WA2706
Duration 2 days
Available Formats Classroom, Virtual

At the end of this training, participants will be able to describe what data architecture is, what is involved in enterprise level data architecture practices, and what the core data architecture activities are. The course is a mixture of lecture and exercises.

  • Data Architecture Stakeholders, Views and Viewpoints
  • Data Requirements Management
  • Data in Architecture and Design Patterns
  • Data Governance
  • Data Modeling

Who Can Benefit

This course is for Architects and Technical Leads requiring an understanding of Data Architecture.

Prerequisites

Knowledge of architecture practices for IT systems is assumed. Some exposure to data systems is beneficial.

Course Details

Outline

Chapter 1 – Data Architecture Stakeholders, Views and Viewpoints

  • Data Architecture Begins with People
  • The Big Picture
  • Who Are the Stakeholders?
  • Stakeholder Identification
  • Stakeholders' Unique Concerns
  • Stakeholder Concerns & Viewpoints
  • Data Architecture Stakeholder Map
  • What Are Views and Viewpoints?
  • Defining Viewpoints
  • Define a Viewpoint: Data Flow Example
  • Standard Notation – ArchiMate
  • ArchiMate Modeling
  • Information Structure View
  • Information and Deployment View
  • Data Flow View
  • Database Deployment View
  • Data Architecture Viewpoints
  • Summary

Chapter 2 – Data and Requirements Management

  • Perspectives on Data Requirements
  • Requirements Management
  • It Starts with Business
  • Knowing Functional Uses
  • Getting the Whole Picture
  • Data Architecture Requirements Process
  • Initiation
  • Feasibility
  • Application Analysis
  • Data Standards
  • Recovery Time and Point Objectives
  • Let’s be SMART!
  • Specific
  • Measurable
  • Achievable
  • Realistic
  • Timely
  • Data Quality Challenges
  • Summary

Chapter 3 – Data Architecture Patterns

  • Do You Love Patterns?
  • Architecture Patterns
  • Where to Begin?
  • Motivations for working with Patterns
  • Pattern Identification Challenge
  • Characteristics of Patterns
  • Data Architecture Pattern Types
  • Recognize the 'Where'
  • Data Patterns Classification
  • Let's Try an Example
  • MDM Patterns
  • MDM Pattern Example
  • Data Architecture Patterns in Use
  • Data Architecture Patterns Categories
  • MDM Pattern Example
  • Data Integration Patterns
  • Data Integration Pattern Example
  • Relationship Between Patterns -Example
  • Knowledge Is Critical
  • It's Not Just About Technology
  • Architecture Saves the Day!
  • Summary

Chapter 4 – Data Governance

  • Objectives
  • Governance
  • Data Governance (DMBOK)
  • Data Governance
  • Deciding if Data Needs Management
  • Meta-data Management
  • Meta-data
  • Examples of Meta-data
  • Importance of Meta-data Management
  • Meta-data Management Best Practices
  • Meta-data Management Tooling
  • Glossary of Business Terms & Data Elements
  • Data Lineage
  • Impact Assessment Via Data Lineage
  • Reference & Master Data Management
  • Systems of Record and Reference
  • Master Data Management Styles
  • Repository Style
  • Registry Style
  • Hybrid Style
  • MDM Project Activities
  • Data Quality Management
  • Data Quality
  • Common Causes of Poor Data Quality
  • Data Quality: Possible Solutions
  • Data Quality Tools
  • Data Quality for “Big Data”
  • Summary

Chapter 5 – Data Modeling

  • Objectives
  • Data Architecture Management
  • Data Development
  • Conceptual Models
  • Logical and Physical Models
  • Formal Models for Organizing Data
  • Classes & Objects
  • Object-Relational Mapping
  • Unified Modeling Language
  • Hierarchical Models
  • Hierarchical Model Example: ERD
  • Data in Motion
  • XML
  • Hierarchical Model Example: XML
  • JSON
  • Hierarchical Model Example: JSON
  • Canonical Models
  • Benefits of Canonical Models
  • Standard Meta-Models
  • Summary
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