Data Vault Mastery: Modernizing Data Warehousing

Modernizing Data Warehousing with Data Vault 2.0 Methodology

Created by: Cu Dinh Tien
Vice Director of IT, Banking/Finance, Software Architect

Created by: Quan Pham
IT expert - Data specialist for banking industry

Created by: Dien Pham
Chief Data Officer, Enterprise Architect, IT Experts

Created by: Educonnhub
Offering premium courses crafted by top experts

What Will I Learn?

• 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

Requirements

• 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.

Target audience

• 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