Advanced Data Vault Modeling Course Overview

To ensure that participants can fully benefit from our Advanced Data Vault Modeling course, it is recommended that they come prepared with the following minimum prerequisites:

• Basic Understanding of Data Warehousing Concepts:

  
  
  • Familiarity with the fundamentals of data warehousing, including the purpose and principles of a data warehouse.

• Knowledge of Data Vault 1.0 or 2.0:

  
  
  • Participants should have a working knowledge of Data Vault modeling, including Hubs, Links, and Satellites.
  • Completion of a foundational course in Data Vault modeling or equivalent practical experience is highly beneficial.

• Experience with Database Design:

  
  
  • Understanding of relational database design and the ability to model data structures.

• SQL Proficiency:

  
  
  • Ability to write and interpret SQL queries, as SQL plays a significant role in Data Vault modeling and querying.

• Familiarity with Business Keys and Relationships:

  
  
  • An understanding of how business keys are used to establish relationships in data models.

• Awareness of Data Integration Concepts:

  
  
  • Knowledge of data integration patterns and practices, including ETL (Extract, Transform, Load) processes.

• Basic Knowledge of Big Data Platforms (for Module 3):

  
  
  • Some awareness of big data platforms, cloud storage solutions, and streaming data technologies is helpful.

• Interest in Data Architecture and Modeling:

  
  
  • A keen interest in learning advanced techniques for data architecture and modeling to solve complex business intelligence challenges.

These prerequisites are intended to provide a foundation for the advanced topics covered in the course. With these basic qualifications, learners will be well-equipped to grasp the advanced concepts and techniques taught in the Advanced Data Vault Modeling course.

Target Audience for Advanced Data Vault Modeling

The Advanced Data Vault Modeling course is tailored for IT professionals focused on next-level data warehouse modeling techniques.

Target Audience Includes:

• Data Architects
• Data Modelers
• Database Administrators
• Business Intelligence Professionals
• Data Analysts
• ETL Developers
• Data Engineers
• Solution Architects
• Data Warehouse Designers
• IT Consultants specializing in data management
• Data Governance Specialists
• Big Data Professionals
• Cloud Data Engineers
• Data Strategists
• Data Science Practitioners looking to understand data infrastructure

Learning Objectives - What you will Learn in this Advanced Data Vault Modeling?

Introduction to Course Learning Outcomes and Concepts Covered

Gain expertise in advanced Data Vault modeling techniques for handling complex data warehousing challenges, including big data, cloud deployments, streaming data, and integrating disparate systems with privacy concerns.

Learning Objectives and Outcomes

• Understand the role of links in modeling events to capture and represent business transactions effectively.
• Learn to design context arrays that enhance satellite structures for richer data context and history.
• Develop strategies for Data Vault modeling that leverage the capabilities of big data platforms, cloud environments, and streaming data architectures.
• Master advanced link design principles to model event-based Units of Work (UOW) accurately.
• Differentiate between the use of hubs and links when modeling keyed instance UOW to maintain data integrity.
• Learn to model addresses and other contexts close to key entities to provide more meaningful business insights.
• Address the challenges of modeling without an Enterprise Key by exploring Anchor and Focal alternatives.
• Discover techniques for modeling shuttle structures to handle fuzzy integration scenarios involving non-exact matching records.
• Develop an understanding of modeling Satellite Books of Knowledge (SAT BOK) with alternate and degenerate keys.
• Learn best practices for modeling Personally Identifiable Information (PII) to ensure compliance with privacy regulations.
• Define and deploy RAW and Business Data Vault (BDV) layers within the architecture to delineate between staged and business-ready data.
• Explore the Ensemble Logical Form (ELF) and virtualization concepts for in-memory data management and real-time data access.
• Identify when and how to apply automation in Data Vault modeling to increase efficiency and maintain consistency across the modeling process.