Data Vault Mastery: Modernizing Data Warehousing

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FinXpertAka Pham

6:59:30

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1 - Introduction

1 - Introduction.mp4

05:02

2 - Course Outline and Key Learning Outcomes.mp4

13:14

3 - Get the Matterials.html

3 - SQL-Commands.zip

3 - SSAS.zip

3 - SSIS.zip

3 - Slide-Master-Data-Vault-Mastery-Modernizing-Data-Warehousing-for-Advanced-Analytics.pdf

2 - Data warehouse Introduction

4 - Enterprise data warehouse environment.mp4

09:06

5 - Introduction to Data Vault.mp4

07:47

6 - Data warehouse architecture.mp4

06:59

3 - Flexible scalable data warehouse architecture

7 - Struggling of data warehouse with changes.mp4

05:05

8 - Data vault 20 architecture.mp4

04:54

9 - Business rules application.mp4

05:03

10 - Staging area layer.mp4

05:46

11 - Data warehouse layer.mp4

05:48

12 - Information mart layer.mp4

05:58

13 - Extension of data vault 20 architecture Metrics Vault.mp4

06:35

14 - Business Vault.mp4

05:51

15 - Operational Vault.mp4

05:52

4 - The data vault 20 methodology

16 - Project planning.mp4

05:49

17 - Project planning Roles Duties.mp4

04:35

18 - Project planning Communication.mp4

03:18

19 - Project planning CMMI maturity model.mp4

04:08

20 - Project planning SCRUM.mp4

02:51

21 - Project planning Estimation of the project.mp4

05:18

22 - Project execution.mp4

07:19

23 - Project execution Implementation steps under agile Scrum methodology.mp4

04:00

5 - The data vault modelling

24 - The data vault modelling.mp4

06:50

25 - Data vault 10 use case requirement database diagram table structure.mp4

10:15

26 - Data vault 10 modelling.mp4

07:07

27 - Data vault 10 hub link satellite ETL load.mp4

13:56

28 - Data vault 20 definition.mp4

11:31

29 - Data vault 20 application hub application.mp4

03:55

30 - Link application Link on Link.mp4

06:06

31 - Link application Same as Link.mp4

03:41

32 - Link application Hierarchical Link.mp4

03:25

33 - Link application Computed Aggregate Link.mp4

03:26

34 - Link application Exploration Link.mp4

03:37

35 - Satellite application Overloaded Satellites.mp4

03:46

36 - Satellite application Multiactive Satellites.mp4

03:11

37 - Satellite application Status tracking Satellites.mp4

03:37

38 - Satellite application Effectively Satellites.mp4

04:01

39 - Satellite application Computed Satellites.mp4

03:42

40 - Advanced data vault modeling PointInTime tables.mp4

04:29

41 - Advanced data vault modeling Bridge tables.mp4

03:49

42 - Data vault 20 flexibility.mp4

04:41

6 - The data vault implementation

43 - Data vault 20 introduction use case implementation.mp4

11:29

44 - Data vault 20 load patterns hub link satellite ETL load.mp4

10:51

45 - Data vault 20 load patterns hash key parallel.mp4

06:59

7 - Dimensional modeling

46 - Dimensional modeling star schemas multidimension schemas dimension design.mp4

12:57

8 - Master data management MDM

47 - Master data management MDM architecture implementation steps.mp4

09:12

9 - Meta data management

48 - Meta data type.mp4

05:12

49 - Metadata capturing Source system.mp4

03:05

50 - Metadata capturing Staging.mp4

03:41

51 - Metadata capturing Metadata for loading hub entities.mp4

07:23

52 - Metadata capturing Metadata for loading link entities.mp4

07:54

53 - Metadata capturing Metadata for loading satellite entities on hubs.mp4

08:53

54 - Metadata capturing Metadata for loading satellite entities on links.mp4

09:46

55 - Metadata capturing Metadata for loading data vault to Datamart.mp4

06:11

10 - Multidimensional database MOLAP cube

56 - Multidimensional database.mp4

04:14

11 - Data warehouse platform

57 - Data warehouse data lake platform updates IBM AWS data platform.mp4

09:13

12 - Handson practices

58 - SSIS load source to Datavault to Datamart to OLAP cube.mp4

58:12

13 - Summary session

59 - Data Vault Mastery Modernizing Data Warehousing for Advanced Analytics.mp4

08:55

Description

Modernizing Data Warehousing with Data Vault 2.0 Methodology

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

Who is this for?

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

What You Need to Know?

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.

More details

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:Â 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.
Who this course is for:
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

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FinXpertAka Pham

Instructor's 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 bankThat 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.I am excited to be your instructor and guide you through the process of building a data strategy roadmap. Together, we will explore the key components of data strategy, develop actionable plans, and navigate the complexities of executing a successful data strategy. I look forward to sharing my expertise with you and helping you unlock the full potential of data within your organization.

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