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This plan includes
- Instant access to 11,000+ online courses
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HD Video Recorded Lectures- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
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This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
- HD Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
What you'll 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
Course Overview
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:
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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.
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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.
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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.
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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.
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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.
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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.
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Discover Dimensional Modeling: Participants will be introduced to dimensional modeling techniques, including star schemas and multi-star schemas, to support reporting and analytics.
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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.
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Unveil Metadata Management: Learners will explore different metadata types and understand how to capture and manage metadata for effective data governance.
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Dive into Multidimensional Databases: Participants will gain insights into the world of multidimensional databases and how they cater to complex analytical queries.
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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.
Pre-requisites
- 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
Curriculum 59 Lectures
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Section 1 : Introduction
- Lecture 2 :
- Course Outline and Key Learning Outcomes
- Lecture 3 :
- Get the Matterials
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Section 2 : Data warehouse Introduction
- Lecture 1 :
- Enterprise data warehouse environment
- Lecture 2 :
- Introduction to Data Vault
- Lecture 3 :
- Data warehouse architecture
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Section 3 : Flexible & scalable data warehouse architecture
- 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
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Section 4 : The data vault 2.0 methodology
- 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
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Section 5 : The data vault modelling
- 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
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Section 6 : The data vault implementation
- 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
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Section 7 : Dimensional modeling
- Lecture 1 :
- Dimensional modeling: star schemas, multi-dimension schemas, dimension design
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Section 8 : Master data management - MDM
- Lecture 1 :
- Master data management: MDM architecture & implementation steps
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Section 9 : Meta data management
- 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
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Section 10 : Multi-dimensional database (MOLAP cube)
- Lecture 1 :
- Multi-dimensional database
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Section 11 : Data warehouse platform
- Lecture 1 :
- Data warehouse - data lake platform updates: IBM & AWS data platform
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Section 12 : Hands-on practices
- Lecture 1 :
- SSIS load: source to Datavault, to Datamart, to OLAP cube
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Section 13 : Summary session
- Lecture 1 :
- Data Vault Mastery Modernizing Data Warehousing for Advanced Analytics
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