Data Vault ReConnect Speed Presenting AM Part Two

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The document discusses using a Data Vault approach for data warehousing large, complex datasets that may be unstructured or streaming. It describes how Data Vault separates the data storage from metadata and schemas to allow for flexibility. It also emphasizes that becoming an agile organization requires changes to both tools and company culture or "DNA". Finally, it compares Data Vault to other data modeling techniques and emphasizes learning from experience.

Data & Analytics Technology

Presenter:
Date:
Note:
Company:
eMail:
Twitter:
Hans Hultgren
June 5, 2014
Genesee Academy
Hans@GeneseeAcademy.com
gohansgo

Difficult to work
with using
standard Tools
& Techniques
* extremely large sets of data
* data structured differently or complex
* streaming & shape-shifting data
“Huge” Data Volumes
n-Structured & Very Complex
Streaming & Shape-Shifting
A
B
C

Schema on Read – versus – Schema on Write
OK, but…
What is the common word?

#SchemaonRead – in the world of Data
Warehousing – means the Metadata for the
Schema must be persisted & historized.

We store the Data before we apply it to a
Model or Schema. This can be in many
different forms. Commonly Doc-Style or #NVP.
H_Entity
Flat File /Blob / Satellite
This field has a listof attributes and maintains a onerecord for one instancerelationshipswith the hub Name Value
Cust_ID 121202
Lname Lundquist
Fname Carl
Add 22 Bird St
City NYC
State NY
Zip 98291
Bdate 10/9/1977
Cust_ID 123335
Lname Dahlgren
Fname Eva
Add 7 Academy
NVP Satellite

Data Vault is well suited to this architecture
since the #BackBone is separated from the
Context. #CSF = Some BK related to data.

Natural Business Correlation. Commonly
related to a true business process. The
relationship between relationships. Peter

Create the Link Relationship based on the Unit
of Work #UOW…

Then consider the Grain or Relationship
between Relationships to refine…

The SALE Hub in this case represents the
business event driven UOW and is #Possessive
of the Links…

Measure of ability to adapt to Change.
or
Overall Performance in adapting to Change.
Data Warehosue Agility
– New and Changing Sources
– New Attributes and Mappings
– New and Changing Transformations
– New and Changing Requirements
– New and Changing Business Rules
– New Forms of Data (n-structured, etc.)
– New and Changing Deliveries
– Expanding Subject Areas

Facts about Agility in your Organization:
An Automation Tool,
A Modeling Approach,
An Agile Aware & Trained Team,
A Project Management Approach, or
All of the above will not make you agile...

Agility in the Organization is a mindset, a
paradigm and it must be in the
#CompanyDNA
People
Process
Tools
Techniques
Agile
Organization
AGILE
DWBI = +

Observations and (other) Lessons Learned
1. DWBI #Maturity plays a big role in the success
2. Agile silos will pop up & can be very compelling
3. #Automation should be planned and purposeful
4. Agility “in” compromises Agility “out” (vice versa)
5. Beware of #Quick&Dirty solutions (they are both)
6. With DV:
1. #ThinkDifferently
2. Try It
3. Tune It

Looking at other Forms of #Ensemble:
Separating the things that change from the
things that don’t change.
DataVault
Anchor
Head &
Version
Focal
Point
Hyper
Agility
2G
2.06NF
Temporal

Three Comparison Criteria. Plus the number of
tables & the #BusinessKey Considerations…

Three Comparison Criteria. Plus the number of
tables & the Business Key Considerations…
Hub Anchor Focal

Presenter:
Date:
Note:
Company:
eMail:
Twitter:
Hans Hultgren
June 5, 2014
Example for ReConnect
Genesee Academy
Hans@GeneseeAcademy.com
gohansgo

IBM Reference models are based on an
#Abstracted #InformationModel with common
core concepts

The goal with the model is to capture the
central #Meaning of the data for a particular
industry and organization

The Data Vault Data Warehouse is ultimately a
physical storage of the enterprise data with
#DataBuckets created to hold data

The Concepts and Fields in the Reference
Model can be #Mapped to the Tables and
Attributes in the Data Vault model

#Lesson: Reference model is huge and complex
#Lesson: Determining #Meaning is time
consuming and must be #TimeBoxed

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Data Vault ReConnect Speed Presenting AM Part Two

1. Presenter: Date: Note: Company: eMail: Twitter: Hans Hultgren June 5, 2014 Genesee Academy Hans@GeneseeAcademy.com gohansgo

2. Difficult to work with using standard Tools & Techniques * extremely large sets of data * data structured differently or complex * streaming & shape-shifting data “Huge” Data Volumes n-Structured & Very Complex Streaming & Shape-Shifting A B C

3. Schema on Read – versus – Schema on Write OK, but… What is the common word?

4. #SchemaonRead – in the world of Data Warehousing – means the Metadata for the Schema must be persisted & historized.

5. We store the Data before we apply it to a Model or Schema. This can be in many different forms. Commonly Doc-Style or #NVP. H_Entity Flat File /Blob / Satellite This field has a listof attributes and maintains a onerecord for one instancerelationshipswith the hub Name Value Cust_ID 121202 Lname Lundquist Fname Carl Add 22 Bird St City NYC State NY Zip 98291 Bdate 10/9/1977 Cust_ID 123335 Lname Dahlgren Fname Eva Add 7 Academy NVP Satellite

6. Data Vault is well suited to this architecture since the #BackBone is separated from the Context. #CSF = Some BK related to data.

7. Presenter: Date: Note: Company: eMail: Twitter: Hans Hultgren June 5, 2014 Genesee Academy Hans@GeneseeAcademy.com gohansgo

8. Natural Business Correlation. Commonly related to a true business process. The relationship between relationships. Peter

9. Create the Link Relationship based on the Unit of Work #UOW…

10. Then consider the Grain or Relationship between Relationships to refine…

11. The SALE Hub in this case represents the business event driven UOW and is #Possessive of the Links…

12. Presenter: Date: Note: Company: eMail: Twitter: Hans Hultgren June 5, 2014 Genesee Academy Hans@GeneseeAcademy.com gohansgo

13. Measure of ability to adapt to Change. or Overall Performance in adapting to Change. Data Warehosue Agility – New and Changing Sources – New Attributes and Mappings – New and Changing Transformations – New and Changing Requirements – New and Changing Business Rules – New Forms of Data (n-structured, etc.) – New and Changing Deliveries – Expanding Subject Areas

14. Facts about Agility in your Organization: An Automation Tool, A Modeling Approach, An Agile Aware & Trained Team, A Project Management Approach, or All of the above will not make you agile...

15. Agility in the Organization is a mindset, a paradigm and it must be in the #CompanyDNA People Process Tools Techniques Agile Organization AGILE DWBI = +

16. Observations and (other) Lessons Learned 1. DWBI #Maturity plays a big role in the success 2. Agile silos will pop up & can be very compelling 3. #Automation should be planned and purposeful 4. Agility “in” compromises Agility “out” (vice versa) 5. Beware of #Quick&Dirty solutions (they are both) 6. With DV: 1. #ThinkDifferently 2. Try It 3. Tune It

17. Presenter: Date: Note: Company: eMail: Twitter: Hans Hultgren June 5, 2014 Genesee Academy Hans@GeneseeAcademy.com gohansgo

18. Looking at other Forms of #Ensemble: Separating the things that change from the things that don’t change. DataVault Anchor Head & Version Focal Point Hyper Agility 2G 2.06NF Temporal

19. Comparing other #Ensemble Forms:

20. Three Comparison Criteria. Plus the number of tables & the #BusinessKey Considerations…

21. Three Comparison Criteria. Plus the number of tables & the Business Key Considerations… Hub Anchor Focal

22. Observations and What have we Learned?

23. Presenter: Date: Note: Company: eMail: Twitter: Hans Hultgren June 5, 2014 Example for ReConnect Genesee Academy Hans@GeneseeAcademy.com gohansgo

24. IBM Reference models are based on an #Abstracted #InformationModel with common core concepts

25. The goal with the model is to capture the central #Meaning of the data for a particular industry and organization

26. The Data Vault Data Warehouse is ultimately a physical storage of the enterprise data with #DataBuckets created to hold data

27. The Concepts and Fields in the Reference Model can be #Mapped to the Tables and Attributes in the Data Vault model

28. #Lesson: Reference model is huge and complex #Lesson: Determining #Meaning is time consuming and must be #TimeBoxed

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