Data Vault Modeling Explained With Use Case

Name: Data Vault Modeling Explained With Use Case
Rating: 3.6 (90 reviews)

Creating Data Vault Model from a real life business use case

Created byPritha Chakraborty

Last updated 6/2022

English

English [Auto],

What you'll learn

Data Vault Modeling
Data Modeling
When to use Data Vault Model
Problems with traditional OLAP Modeling
Step By Step Data Vault Model Creation from a Business Case
How to design a GDPR complaint Data Architecture using Data Vault approach

Course content

1 section • 11 lectures • 1h 48m total length

Introduction0:34
Explore data vault modeling and its rising popularity, with a concise introduction and a quick discussion on how this approach can be used in practice.
What is Data Vault1:09
The lecture presents data vault as a scalable data modeling approach, contrasts it with dimensional models and star schema, and notes using a dimensional layer and views for reporting.
Explain the use case with traditional star schema1:46
Explore a traditional star schema for a sales use case, with customer, product, and time dimensions and a fact table of unit and revenue for key reports.
Problems with traditional data warehouse model2:52
Data vault modeling offers an extensible data warehouse layer between staging and final dimensions, avoiding destructive loads while accommodating country-specific attributes.
Raw Vault Vs Business Vault2:52
Explore raw data vault versus business vault within data vault modeling; see how sources are extracted and staged into a data warehouse built from hub, satellite, and link components.
Step By Step Data Vault Modeling42:29
Explains data vault modeling with hubs, satellites, and links, using business keys, hash keys, and timestamps to build scalable, agile warehouses and reporting via the line item hub and views.
Sample SQL to get report6:15
Learn to query data vault structures to compute total revenue for U.S. Customs by chaining hub, link, and satellite joins (via customer) and using a materialized view to speed dashboards.
How to design a GDPR complaint Data Architecture using Data Vault approach19:58
Design a GDPR-compliant data architecture using a Data Vault approach by separating sensitive and non-sensitive data into satellites linked to hubs, enabling selective masking and deletion.
Business Data Vault : PIT, Bridge, Reference table16:04
Explore how business data and materialized views speed dashboards by lookups and mappings, while data vault elements (hubs, satellites, links, bridges, PIT, and reference tables) enable scalable reporting.
When To Use Data Vault and When Not To Use Data Vault7:59
Use data vault for long-term, scalable data warehousing with multiple sources, frequent schema changes, and privacy controls. Avoid it for short-term, simple OLTP needs.
Data Modeling Tools6:44
Explore data modelling tools for data vault modeling, from cloud and on-premises options. Learn to design hubs and satellites, hash functions, and load procedures using diagram-based UI tools.

Requirements

Very basic knowledge of databases

Description

This is a beginner course for understanding Data Vault Modeling.

This course describes below topics

1) What is Data Vault : Generic Definition,

Data Vault is an agile system of business intelligence built to solve inadequacies in the Data Warehouse. Data Vault is one of the most popular methodologies when it comes to developing and maintaining a Data Warehouse.

2) What is the problem with traditional OLAP modeling i.e star schema : Problems such as scalability, non-agility, GDPR implementation.

3) How to resolve those problems with Data Vault Modeling.

4) Step by step process to create the data vault model.

5) Benefits of Data Vault, Drawbacks of Data Vault.

6) What is GDPR or General Data Protection Regulation?

General Definition GDPR outlines the specific rights that ensure individuals are in control of their personal data, can request a copy of their data at any time, and can ask for it to be deleted. GDPR will also mean individuals have to give consent before organizations can contact them - putting an end to random direct mail and companies selling data on to others.

7) Problems with implementing GDPR compliant data architecture.

8) Designing GDPR compliant data model using the data vault approach.

9) What is Business data Vault. Designing Bridge, PIT and Reference tables

10) When to use Data Vault Modeling and when not to use Data Vault approach.

11) Data Vault Modeling Tools

All the concepts mentioned above are explained in the course with examples.

After completing the course, the learner will be able to design data vault models and will be able to answers the above-mentioned points.

Who this course is for:

Data Modelers,Data Architects,Data Analysts, Interview Preparation,Software Engineers,Students