Data Vault Mastery

Modernizing Data Warehousing for Advanced

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

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.

Description

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, etcCourse 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 servicesBy 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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Outline and Key Learning Outcomes

Lecture 3 Get the Matterials

Section 2: Data warehouse Introduction

Lecture 4 Enterprise data warehouse environment

Lecture 5 Introduction to Data Vault

Lecture 6 Data warehouse architecture

Section 3: Flexible & scalable data warehouse architecture

Lecture 7 Struggling of data warehouse with changes

Lecture 8 Data vault 2.0 architecture

Lecture 9 Business rules application

Lecture 10 Staging area layer

Lecture 11 Data warehouse layer

Lecture 12 Information mart layer

Lecture 13 Extension of data vault 2.0 architecture >> Metrics Vault

Lecture 14 Business Vault

Lecture 15 Operational Vault

Section 4: The data vault 2.0 methodology

Lecture 16 Project planning

Lecture 17 Project planning >> Roles & Duties

Lecture 18 Project planning >> Communication

Lecture 19 Project planning >> CMMI maturity model

Lecture 20 Project planning >> SCRUM

Lecture 21 Project planning >> Estimation of the project

Lecture 22 Project execution

Lecture 23 Project execution >> Implementation steps under agile - Scrum methodology

Section 5: The data vault modelling

Lecture 24 The data vault modelling

Lecture 25 Data vault 1.0 use case, requirement, database diagram & table structure

Lecture 26 Data vault 1.0 modelling

Lecture 27 Data vault 1.0 hub, link, satellite, ETL load

Lecture 28 Data vault 2.0 definition

Lecture 29 Data vault 2.0 application >> hub application

Lecture 30 Link application >> Link on Link

Lecture 31 Link application >> Same as Link

Lecture 32 Link application >> Hierarchical Link

Lecture 33 Link application >> Computed Aggregate Link

Lecture 34 Link application >> Exploration Link

Lecture 35 Satellite application >> Overloaded Satellites

Lecture 36 Satellite application >> Multi-active Satellites

Lecture 37 Satellite application >> Status tracking Satellites

Lecture 38 Satellite application >> Effectively Satellites

Lecture 39 Satellite application >> Computed Satellites

Lecture 40 Advanced data vault modeling >> Point-In-Time tables

Lecture 41 Advanced data vault modeling >> Bridge tables

Lecture 42 Data vault 2.0 flexibility

Section 6: The data vault implementation

Lecture 43 Data vault 2.0 introduction & use case implementation

Lecture 44 Data vault 2.0 load patterns >> hub, link, satellite, ETL load

Lecture 45 Data vault 2.0 load patterns >> hash key & parallel

Section 7: Dimensional modeling

Lecture 46 Dimensional modeling: star schemas, multi-dimension schemas, dimension design

Section 8: Master data management - MDM

Lecture 47 Master data management: MDM architecture & implementation steps

Section 9: Meta data management

Lecture 48 Meta data type

Lecture 49 Metadata capturing >> Source system

Lecture 50 Metadata capturing >> Staging

Lecture 51 Metadata capturing >> Metadata for loading hub entities

Lecture 52 Metadata capturing >> Metadata for loading link entities

Lecture 53 Metadata capturing >> Metadata for loading satellite entities on hubs

Lecture 54 Metadata capturing >> Metadata for loading satellite entities on links

Lecture 55 Metadata capturing >> Metadata for loading data vault to Datamart

Section 10: Multi-dimensional database (MOLAP cube)

Lecture 56 Multi-dimensional database

Section 11: Data warehouse platform

Lecture 57 Data warehouse - data lake platform updates: IBM & AWS data platform

Section 12: Hands-on practices

Lecture 58 SSIS load: source to Datavault, to Datamart, to OLAP cube

Section 13: Summary session

Lecture 59 Data Vault Mastery Modernizing Data Warehousing for Advanced Analytics

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