The document discusses Data Vault 2.0, a model designed for historical tracking and data management in enterprise data warehouses, featuring a flexible and scalable architecture that integrates both SQL and NoSQL systems. It introduces MD5 hashes for change data capture, simplifying column comparisons to a single operation and ensuring consistency across platforms. The new methodology also incorporates modern practices such as agile delivery and project oversight standards.

1. Data Vault 2.0: Using MD5 Hashes for
Change Data Capture
Kent Graziano
Data Warrior LLC
Twitter @KentGraziano

2. Data Vault Definition
The Data Vault is a detail oriented, historical tracking
and uniquely linked set of normalized tables that
support one or more functional areas of business.
It is a hybrid approach encompassing the best of
breed between 3rd normal form (3NF) and star
schema. The design is flexible, scalable, consistent
and adaptable to the needs of the enterprise.
Architected specifically to meet the needs
of today’s enterprise data warehouses
Dan Linstedt: Defining the Data Vault
TDAN.com Article

3. Data Vault Time Line
E.F. Codd invented
relational modeling
Chris Date and
Hugh Darwen
Maintained and
Refined
Modeling
1976 Dr Peter Chen
Created E-R
Diagramming
Mid 70’s AC Nielsen
Popularized
Dimension & Fact Terms
1990 – Dan Linstedt
Begins R&D on Data
Vault Modeling
1960 1970 1980 1990 2000
Early 70’s Bill
Inmon Began
Discussing Data
Warehousing
Mid 60’s Dimension & Fact
Modeling presented by
General Mills and Dartmouth
University
Late 80’s – Barry
Devlin and Dr Kimball
Release “Business
Data Warehouse”
Mid 80’s Bill Inmon
Popularizes Data
Warehousing
Mid – Late 80’s Dr Kimball
Popularizes Star Schema
2000 – Dan Linstedt
releases first 5
articles on Data Vault
Modeling
© LearnDataVault.com

4. 2014 - Next Evolution

5. What’s New in DV2.0?
 Modeling Structure Includes…
● NoSQL, and Non-Relational DB systems, Hybrid Systems
● Minor Structure Changes to support NoSQL
 New ETL Implementation Standards
● For true real-time support
● For NoSQL support
 New Architecture Standards
● To include support for NoSQL data management systems
 New Methodology Components
● Including CMMI, Six Sigma, and TQM
● Including Project Planning, Tracking, and Oversight
● Agile Delivery Mechanisms
● Standards, and templates for Projects
© LearnDataVault.com

6. This model is fully
compliant with Hadoop,
needs NO changes to
work properly.
The Hash Keys can be
used to join to Hadoop
data sets.
MD5 PK – replaces
surrogate keys
MD5DIFF – used for
change detection
Use of MD5 Hash in DV2.0
© LearnDataVault.com

7. MD5-based Change Detection
 Think Type 2 SCD
 Old Way:
● Compare column by column
● Source value != Current value in DW table
● 20 columns, then 20 compares
 New Way:
● Concatenate all columns to one string
● Convert to one char(32) string with hash function
● Compare to hashed value (MD5DIFF) in target table
● Does not matter how many columns
© Data Warrior LLC

8. What does it look like?
 Encode using standard MD5 hash
function
● rawtohex(sys.utl_raw.cast_to_raw(
dbms_obfuscation_toolkit.md5 (input_string =>
...)
 Need to minimize chance of duplicates
● 12||3||45 and 1||2||345 hash to same value
● Need a separator between each
● Also handles case of null values
● Example: Col1||’^’||Col2||’^’||Col3
© Data Warrior LLC

9. Other considerations
 To generate most consistent string: standardize!
 Convert data types
 If 'NUMBER', 'NVARCHAR2', 'NVARCHAR',
'NCHAR‘
● THEN 'TO_CHAR(' || column_name || ')‘
 If 'RAW‘
● THEN 'ENC_BASE64(' || column_name || ')‘
 If 'DATE‘
● THEN 'TO_CHAR(' || column_name || ', ''YYYY-MM-DD'')‘
 If LIKE 'TIME%‘
● THEN 'TO_CHAR(' || column_name || ', ''YYYY-MM-DD
HH24:MI:SS'')'
© Data Warrior LLC

10. Final Input String
(UPPER(TRIM(T1.GENERICNAME))
||'^'||
UPPER(TRIM(
TO_CHAR(T1.MED_STRNG_AMT)))
||'^'||
UPPER(TRIM(T1.UOM_CD))
||'^'||
UPPER(TRIM(T1.MED_FORM_NM))
||'^')
© Data Warrior LLC

11. So what?
 MD5 hash is consistent cross-platform
 Changes multi-column compares to a single
column
 All compares take the same time during load
process
 Can use with any DW architecture that requires
change detections
 Virtually no limit
● Think Big Data/Hadoop/NoSQL
 Can generate the input string automatically
● But that is another talk!
© Data Warrior LLC

12. Learn more about Data Vault
www.LearnDataVault.com
www.danlinstedt.com
On YouTube:
www.youtube.com/LearnDataVault
On Facebook:
www.facebook.com/learndatavault

13. Super Charge Your Data Warehouse
Available on Amazon.com
Soft Cover or Kindle Format
Now also available in PDF at
LearnDataVault.com

14. Contact Information
Kent Graziano
The Oracle Data Warrior
Data Warrior LLC
Kent.graziano@att.net
On Twitter @KentGraziano
Visit my blog at
http://kentgraziano.com