Data Vault 2.0 is more than a modeling approach, it is an invaluable methodology that adds value to an array of data warehouse projects. Join Michael Olschimke as he describes the positive impact of Data Vault 2.0 to data warehousing teams. This session also includes a short demonstration of Data Vault Express, a product proven to automate the entire data vault lifecycle to deliver data vault solutions to the business faster, at lower cost and with less risk. Join us and learn how you can make Data Vaults a practical reality. Meet the Speaker = = = = = = = = = Michael Olschimke has more than 20 years of experience in Information Technology. During the last eight years, he has specialized in Business Intelligence topics such as OLAP, Dimensional Modelling, and Data Mining and holds a Master of Science in Information Systems from Santa Clara University in Silicon Valley, California. Michael is one of the Chief Executive Officer (CEO) and co-founders of Scalefree where he is responsible for the business direction of the company. He is also co-author of the book "Building a Scalable Data Warehouse with Data Vault 2.0".

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Will Data Vault add Value to your Data Warehouse?
3 signs the answer is “yes”!

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Chief Executive Officer (Scalefree International GmbH)
Michael Olschimke
Education
● M.Sc. Information Systems (Santa
Clara University in Silicon Valley)
● Diploma Computer Science
(University of Applied Sciences in
Bingen, Germany)
● Authorised Data Vault 2.0 Trainer
● Certified Data Vault 2.0 Practitioner
(CDVP2™)
Core Competencies
● Expert within Data Warehousing and Data Vault 2.0
● Certified Data Vault 2.0 Practitioner, Trainer
● Reviewer for Data Vault 2.0 Implementations & Tools
Experience
More than 20 years of experience in Information Technology to
lead Scalefree
as CEO.

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Sign 1:
Data Vault is more than a Modeling Approach

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Three Basic Entity Types
DATA VAULT STRUCTURE OVERVIEW
● Hub
● Link
● Satellite
LINK FLIGHT
ORIGIN DEST
SAT FLIGHT
HUB AIRPORT
HUB CARRIER
SAT CARRIER REG
SAT FLIGHT STATUS
HUB FLIGHT
SAT AIRPORT TZ
SAT AIRPORT
LOCATION

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A System of Business Intelligence
containing the necessary components
needed to accomplish enterprise vision
in Data Warehousing and Information
Delivery.
“
DATA VAULT 2.0 DEFINITION:

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•Consistent
•Repeatable
•Pattern Based
METHODOLOGY
•Multi-Tier
•Scalable
•Supports NoSQL
Implementation
•Flexible
•Scalable (Big Data)
•Hub & Spoke
ARCHITECTURE
MODEL
A SYSTEM OF BUSINESS
INTELLIGENCE COMPRISED OF…

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Repeatable Consistent Standardized
AGILE METHODOLOGY
IS AUTOMATION

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Enhances
De-Coupling
Ensures Low
Impact
Changes
Provides
Managed
Self-Service BI
ARCHITECTURE
BENEFITS
Includes Hybrid
NoSQL
Platforms

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BENEFITS
● Follows Scale Free
Architecture
● Based on Hub & Spoke
Design
● Backed by Set Logic &
MPP Math
HUB SATELLITE
SATELLITE
LINK
LINK
HUB

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EVER GROWING DIMENSIONAL
WAREHOUSE COSTS
Why can’t you just copy the
model and build one for ME?
Why can’t you just add my fields
to the existing dimension? Why
does it have to conform?
What do you mean
re-state the data and
re-test it all?
3 MONTHS, $100 K
5 MONTHS, $250 K
18 MONTHS, $500 K
COST: $ K
TIME IN
MONTH

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Sign 2:
The Positive Impact On Data Warehousing Teams

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THE POSITIVE IMPACT ON DATA WAREHOUSING TEAMS
1. Focus On Business Value
● Sprints oriented on business value
● Rather than non-functional requirements
Berenberg case
https://scalefr.ee/data-debates
Technical debt piled up

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THE POSITIVE IMPACT ON DATA WAREHOUSING TEAMS
2. Feasible Solutions
● Data Vault 2.0 is like a trove of solutions for issues
How to
continuously
integrate and
deploy?
How to
automate?
How to load
source
systems
independently
?
How to (unit)
test a data
warehouse?
How to build
a fully
auditable
data
warehouse?

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THE POSITIVE IMPACT ON DATA WAREHOUSING TEAMS
3. New Technologies
● Data Vault 2.0 works well with NoSQL databases
● Designed (but not limited to) MPP databases
● Used to integrate on-premise with cloud solutions
Example:
Automotive parts producer in the Fortune 500 integrates on-premise production
and customer data with sensor data in the cloud

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THE POSITIVE IMPACT ON DATA WAREHOUSING TEAMS
4. Repeating Patterns
● The Data Vault 2.0 model is based on 15+ simple patterns
● Based on three base patterns (hubs, links, satellites)
● Implementation is based on patterns
● Methodology is based on patterns
Example:
Pattern has been derived from nature

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THE POSITIVE IMPACT ON DATA WAREHOUSING TEAMS
5. Higher Quality
● Patterns are found in implementation
● Possible to automate and generate
● Less deviations in implementation (compared to manual implementation)
Standards were
introduced and
automated
Multiple consulting firms
on site…
Customer had multiple
data warehouse projects
in parallel

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THE POSITIVE IMPACT ON DATA WAREHOUSING TEAMS
6. More Automation
● Possible to automate 100 % of data lake (or relational staging area)
● Possible to automate 100 % or Raw Data Vault
● Possible to automate some parts of the Business Vault and information marts
Example:
Grenke (and countless others) uses Data Vault to speed up the delivery of data
assets

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Sign 3:
Automate The Entire Data Vault Lifecycle