Course #:WA2706 Data Architecture Foundations Training 07/06/2020 - 07/07/2020 USD$1,395.00 Instructor Led Virtual Guaranteed to Run! 08/17/2020 - 08/18/2020 USD$1,395.00 Instructor Led Virtual 09/14/2020 - 09/15/2020 USD$1,395.00 Instructor Led Virtual What You Will Learn 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. The exercises will have participants work through a case study to apply their knowledge of the data architecture concepts to deepen their understanding of data architecture work. Duration 2 days Topics Data Architecture Stakeholders, Views and Viewpoints Data Requirements Management Data in Architecture and Design Patterns Data Governance Data Modeling Audience 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. Outline of Data Architecture Foundations Training 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 We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC. View Course Outline Share This Request On-Site or Customized Course Info REGISTER FOR A COURSEWARE SAMPLE x Sent First Name Last Name Email Request On-Site or Customized Course Info x Sent First Name Last Name Phone Number Company Name Email Question
Course #:WA2706 Data Architecture Foundations Training 07/06/2020 - 07/07/2020 USD$1,395.00 Instructor Led Virtual Guaranteed to Run! 08/17/2020 - 08/18/2020 USD$1,395.00 Instructor Led Virtual 09/14/2020 - 09/15/2020 USD$1,395.00 Instructor Led Virtual What You Will Learn 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. The exercises will have participants work through a case study to apply their knowledge of the data architecture concepts to deepen their understanding of data architecture work. Duration 2 days Topics Data Architecture Stakeholders, Views and Viewpoints Data Requirements Management Data in Architecture and Design Patterns Data Governance Data Modeling Audience 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. Outline of Data Architecture Foundations Training 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 We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC. View Course Outline Share This Request On-Site or Customized Course Info REGISTER FOR A COURSEWARE SAMPLE x Sent First Name Last Name Email Request On-Site or Customized Course Info x Sent First Name Last Name Phone Number Company Name Email Question
