Master Data Vault: Modernize Your Data Warehousing Skills

Data Vault Mastery: Modernizing Data Warehousing

Modernizing Data Warehousing with Data Vault 2.0 Methodology

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Last Updated: 2024-11-27 05:11:25 English Course Type: Self Learning

Modernizing Data Warehousing for Advanced Analytics with the powerful methodology of Data Vault 2.0
Scalable Data Vault 2.0 data warehouse architecture
Data Vault 2.0 methodology in discussing project planning & execution
How to Modelling Data Vault 1.0 & 2.0
The real practical of Data Vault 2.0 with Loading Patterns, ETL Load, HashKey
How to design Dimensional Model
Master Data Management from architecture to implementation steps
Meta data management on each data layers and how to capture metadata
What is Multi-dimensional Database (OLAP CUBE)
Update Enterprise Data warehouse (DWH) Platform from IBM, AWS and Data Vault 2.0 technology landscape
Hands-On Lab with loading source to datavautl, to datamart and to OLAP CUBE by using SQL Server, SSIS, SSAS

Course Overview:

Data Vault Mastery: Modernizing Data Warehousing for Advanced Analytics is an in-depth and comprehensive training program designed to equip participants with the skills and knowledge required to leverage the power of data vault methodologies in modern data warehousing environments. This course focuses on the latest advancements in data vault 2.0, providing learners with a solid foundation in data modeling, implementation, and management techniques for supporting advanced analytics.

You also have a chance to open knowledge on other data aspects such as: Master Data Management (MDM), Metadata Management, Multidimensional Databases and Data Warehouse Platform, etc

Course Objectives:

Understand the Fundamentals: Participants will grasp the core concepts of data warehousing, data vault methodologies, and the need for modernization in the era of advanced analytics.
Master Data Vault 2.0 Architecture: Learners will explore the architecture of Data Vault 2.0 and understand how it addresses scalability, flexibility, and adaptability for handling dynamic data environments.
Learn Data Vault Modeling: The course delves into Data Vault 2.0 modeling techniques, covering the design of hubs, links, and satellites to capture historical data and manage changes.
Implement Data Vault Load Patterns: Participants will gain hands-on experience in implementing Data Vault 2.0 load patterns for efficiently loading data from various sources into the data warehouse.
Explore Data Vault Physical ETL Load: The course provides insights into the physical implementation of ETL (Extract, Transform, Load) processes for populating the Data Vault.
Understand Data Vault 2.0 Hash Key: Learners will learn about the significance of hash keys in Data Vault 2.0 for enhancing data performance and managing data integrity.
Discover Dimensional Modeling: Participants will be introduced to dimensional modeling techniques, including star schemas and multi-star schemas, to support reporting and analytics.
Master Data Management: The course covers the architecture and development steps of Master Data Management (MDM) to ensure consistent and accurate master data across the organization.
Unveil Metadata Management: Learners will explore different metadata types and understand how to capture and manage metadata for effective data governance.
Dive into Multidimensional Databases: Participants will gain insights into the world of multidimensional databases and how they cater to complex analytical queries.
Explore Data Warehouse Platforms: The course examines the technology landscape of Data Vault 2.0, IBM's Data & Analytics products, and AWS Data & Analytics services

By the end of the "Data Vault Mastery: Modernizing Data Warehousing for Advanced Analytics" course, participants will be well-equipped to design, implement, and manage robust data vault structures to support advanced analytics and derive valuable insights from their data assets.

Basic Knowledge of Data Warehousing: Familiarity with data warehousing concepts, including the purpose and architecture of data warehouses, data modeling, and ETL processes, will be helpful.
Database Fundamentals: Understanding of fundamental database concepts, such as tables, relationships, and SQL queries, is beneficial.
Business Intelligence and Analytics: Some knowledge of business intelligence tools and analytics concepts can be advantageous for understanding the application of data vault methodology in advanced analytics.
Data Modeling: Familiarity with data modeling techniques, such as entity-relationship diagrams and dimensional modeling, can be beneficial for comprehending the concepts taught in the course.
Database Management Systems: Basic knowledge of database management systems (e.g., Oracle, SQL Server, etc.) is recommended, as data vault implementation may involve working with different databases.
Data Integration: Awareness of data integration processes and tools, such as ETL (Extract, Transform, Load), is helpful for understanding data vault load patterns.

Data Warehouse Architects
Data Engineers
Business Intelligence (BI) Developers
Data Analysts
Data Scientists
Data Managers and Data Governance Professionals
IT Managers and Professionals
Data Modellers

Section 1 : Introduction 3 Lectures 00:00:00
Lecture 1 :
Introduction Preview
Lecture 2 :
Course Outline and Key Learning Outcomes
Lecture 3 :
Get the Matterials
Section 2 : Data warehouse Introduction 3 Lectures 00:00:00
Lecture 1 :
Enterprise data warehouse environment
Lecture 2 :
Introduction to Data Vault
Lecture 3 :
Data warehouse architecture
Section 3 : Flexible & scalable data warehouse architecture 9 Lectures 00:00:00
Lecture 1 :
Struggling of data warehouse with changes
Lecture 2 :
Data vault 2.0 architecture
Lecture 3 :
Business rules application
Lecture 4 :
Staging area layer
Lecture 5 :
Data warehouse layer
Lecture 6 :
Information mart layer
Lecture 7 :
Extension of data vault 2.0 architecture >> Metrics Vault
Lecture 8 :
Business Vault
Lecture 9 :
Operational Vault
Section 4 : The data vault 2.0 methodology 8 Lectures 00:00:00
Lecture 1 :
Project planning
Lecture 2 :
Project planning >> Roles & Duties
Lecture 3 :
Project planning >> Communication
Lecture 4 :
Project planning >> CMMI maturity model
Lecture 5 :
Project planning >> SCRUM
Lecture 6 :
Project planning >> Estimation of the project
Lecture 7 :
Project execution
Lecture 8 :
Project execution >> Implementation steps under agile - Scrum methodology
Section 5 : The data vault modelling 19 Lectures 00:00:00
Lecture 1 :
The data vault modelling
Lecture 2 :
Data vault 1.0 use case, requirement, database diagram & table structure
Lecture 3 :
Data vault 1.0 modelling
Lecture 4 :
Data vault 1.0 hub, link, satellite, ETL load
Lecture 5 :
Data vault 2.0 definition
Lecture 6 :
Data vault 2.0 application >> hub application
Lecture 7 :
Link application >> Link on Link
Lecture 8 :
Link application >> Same as Link
Lecture 9 :
Link application >> Hierarchical Link
Lecture 10 :
Link application >> Computed Aggregate Link
Lecture 11 :
Link application >> Exploration Link
Lecture 12 :
Satellite application >> Overloaded Satellites
Lecture 13 :
Satellite application >> Multi-active Satellites
Lecture 14 :
Satellite application >> Status tracking Satellites
Lecture 15 :
Satellite application >> Effectively Satellites
Lecture 16 :
Satellite application >> Computed Satellites
Lecture 17 :
Advanced data vault modeling >> Point-In-Time tables
Lecture 18 :
Advanced data vault modeling >> Bridge tables
Lecture 19 :
Data vault 2.0 flexibility
Section 6 : The data vault implementation 3 Lectures 00:00:00
Lecture 1 :
Data vault 2.0 introduction & use case implementation
Lecture 2 :
Data vault 2.0 load patterns >> hub, link, satellite, ETL load
Lecture 3 :
Data vault 2.0 load patterns >> hash key & parallel
Section 7 : Dimensional modeling 1 Lectures 00:00:00
Lecture 1 :
Dimensional modeling: star schemas, multi-dimension schemas, dimension design
Section 8 : Master data management - MDM 1 Lectures 00:00:00
Lecture 1 :
Master data management: MDM architecture & implementation steps
Section 9 : Meta data management 8 Lectures 00:00:00
Lecture 1 :
Meta data type
Lecture 2 :
Metadata capturing >> Source system
Lecture 3 :
Metadata capturing >> Staging
Lecture 4 :
Metadata capturing >> Metadata for loading hub entities
Lecture 5 :
Metadata capturing >> Metadata for loading link entities
Lecture 6 :
Metadata capturing >> Metadata for loading satellite entities on hubs
Lecture 7 :
Metadata capturing >> Metadata for loading satellite entities on links
Lecture 8 :
Metadata capturing >> Metadata for loading data vault to Datamart
Section 10 : Multi-dimensional database (MOLAP cube) 1 Lectures 00:00:00
Lecture 1 :
Multi-dimensional database
Section 11 : Data warehouse platform 1 Lectures 00:00:00
Lecture 1 :
Data warehouse - data lake platform updates: IBM & AWS data platform
Section 12 : Hands-on practices 1 Lectures 00:00:00
Lecture 1 :
SSIS load: source to Datavault, to Datamart, to OLAP cube
Section 13 : Summary session 1 Lectures 00:00:00
Lecture 1 :
Data Vault Mastery Modernizing Data Warehousing for Advanced Analytics

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Daniel Pham,

7448 Course Views

8 Courses

I am an experienced and accomplished author with a deep passion for data strategy and management. With over 15 years of hands-on experience in the IT industry, I have gained extensive knowledge and expertise in various domains, including banking, insurance, retail, and more. Throughout my career, I have held several senior positions such as: - Data Division Director of an insurance company - Deputy Data Governance & Analytics of the Group (Real Estate, Retail, Hospitality, Commerce, ...) - Deputy Chief Data Officer cum Senior IT Strategy Expert - Head of Data & Analytics Service - Project Director/ Project Manager - Enterprise & Solution Architect of bank That have allowed me to make significant contributions to the field. And right now, as a director of Data Division, I played a pivotal role in shaping data initiatives and driving the effective use of data within organizations. Additionally, as a Data Governance & Analytics expert, I focused on establishing robust data governance frameworks and harnessing the power of analytics to drive actionable insights. With my diverse background and extensive industry experience, I have developed a strong understanding of the challenges and opportunities in the data management landscape. Through my work as an author, I am dedicated to sharing my knowledge, insights, and practical strategies with students like you.