Agile Data Warehouse Modeling: Introduction to Data Vault Data Modeling
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Transcript of Agile Data Warehouse Modeling: Introduction to Data Vault Data Modeling
Agile Data Warehouse Modeling:Introduction to Data Vault Modeling
Kent Graziano
Data Warrior LLC
Twitter @KentGraziano
Agenda
Bio What do we mean by Agile? What is a Data Vault? Where does it fit in an DW/BI architecture How to design a Data Vault model Being “agile”
#OUGF14
My Bio
Oracle ACE Director Certified Data Vault Master and DV 2.0 Architect Member: Boulder BI Brain Trust Data Architecture and Data Warehouse Specialist
● 30+ years in IT● 25+ years of Oracle-related work● 20+ years of data warehousing experience
Co-Author of ● The Business of Data Vault Modeling ● The Data Model Resource Book (1st Edition)
Past-President of ODTUG and Rocky Mountain Oracle User Group
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Manifesto for Agile Software Development
● “We are uncovering better ways of developing software by doing it and helping others do it.
● Through this work we have come to value:
● Individuals and interactions over processes and tools
● Working software over comprehensive documentation
● Customer collaboration over contract negotiation
● Responding to change over following a plan
● That is, while there is value in the items on the right, we value the items on the left more.”
● http://agilemanifesto.org/
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Applying the Agile Manifesto to DW
● User Stories instead of requirements documents
● Time-boxed iterations● Iteration has a standard length● Choose one or more user stories to fit in that
iteration
● Rework is part of the game● There are no “missed requirements”... only
those that haven’t been delivered or discovered yet.
(C) Kent Graziano#OUGF14
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.
Dan Linstedt: Defining the Data VaultTDAN.com Article
Architected specifically to meet the needs of today’s enterprise data warehouses
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What is Data Vault Trying to Solve?
● What are our other Enterprise
Data Warehouse options?● Third-Normal Form (3NF): Complex
primary keys (PK’s) with cascading
snapshot dates
● Star Schema (Dimensional): Difficult to
reengineer fact tables for granularity
changes
● Difficult to get it right the first
time
● Not adaptable to rapid
business change
● NOT AGILE!
(C) Kent Graziano#OUGF14
Data Vault Time Line
20001960 1970 1980 1990
E.F. Codd invented relational modeling
Chris Date and Hugh Darwen Maintained and Refined Modeling
1976 Dr Peter ChenCreated E-R Diagramming
Early 70’s Bill Inmon Began Discussing Data Warehousing
Mid 60’s Dimension & Fact Modeling presented by General Mills and Dartmouth University
Mid 70’s AC Nielsen PopularizedDimension & Fact Terms
Mid – Late 80’s Dr Kimball Popularizes Star Schema
Mid 80’s Bill InmonPopularizes Data Warehousing
Late 80’s – Barry Devlin and Dr Kimball Release “Business Data Warehouse”
1990 – Dan Linstedt Begins R&D on Data Vault Modeling
2000 – Dan Linstedt releases first 5 articles on Data Vault Modeling
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Data Vault Evolution
The work on the Data Vault approach began in the early 1990s, and completed around 1999.
Throughout 1999, 2000, and 2001, the Data Vault design was tested, refined, and deployed into specific customer sites.
In 2002, the industry thought leaders were asked to review the architecture.
● This is when I attend my first DV seminar in Denver and met Dan!
In 2003, Dan began teaching the modeling techniques to the mass public.
Now in 2014, Dan introduced DV 2.0!(C) Kent Graziano
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Where does a Data Vault Fit?
Staging
EDWDATA VAULT
Data Marts
(Star Schemas)
Data Marts
(Star Schemas)
Data Marts
(Star Schemas)
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Where does Data Vault fit?
Data Vault goes here#OUGF14
How to be Agile using DV
● Model iteratively● Use Data Vault data modeling technique● Create basic components, then add over time
● Virtualize the Access Layer● Don’t waste time building facts and dimensions up front● ETL and testing takes too long● “Project” objects using pattern-based DV model with
database views (or BI meta layer)
● Users see real reports with real data● Can always build out for performance in
another iteration
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Data Vault: 3 Simple Structures
1 Hub
2 Link
3Satellite
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Data Vault Core Architecture
Hubs = Unique List of Business Keys Links = Unique List of Relationships across
keys Satellites = Descriptive Data
Satellites have one and only one parent table Satellites cannot be “Parents” to other tables Hubs cannot be child tables
© LearnDataVault.com#OUGF14
Common Attributes
Required – all structures● Primary key – PK● Load date time stamp – DTS● Record source – REC_SRC
Required – Satellites only● Load end date time stamp – LEDTS● Optional in DV 2.0
Optional – Extract Dates –Extrct_DTS Optional – Hubs & Links only
● Last seen dates – LSDTs● MD5KEY
Optional – Satellites only● Load sequence ID – LDSEQ_ID● Update user – UPDT_USER● Update DTS – UPDT_DTS● MD5DIFF
© LearnDataVault.com#OUGF14
1. Hub = Business Keys
Hubs = Unique Lists of Business KeysBusiness Keys are used to TRACK and IDENTIFY key informationNew: DV 2.0 includes MD5 of the BK to link to Hadoop/NoSQL
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2: Links = Associations
Links = Transactions and Associations
They are used to hook together multiple sets of information
In DV 2.0 the BK attributes migrate to the Links for faster query
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Modeling Links - 1:1 or 1:M?
Today:
● Relationship is a 1:1 so why model a Link?
Tomorrow:
● The business rule can change to a 1:M.
● You discover new data later.
With a Link in the Data Vault:
● No need to change the EDW structure.
● Existing data is fine.
● New data is added.
(C) Kent Graziano#OUGF14
3. Satellites = Descriptors
• Satellites provide context for the Hubs and the Links
• Tracks changes over time
• Like SCD 2
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This model is partially compliant with Hadoop.
The Hash Keys can be used to join to Hadoop data sets.
Note: Business Keys replicated to the Link structure for “join” capabilities on the way out to Data Marts.
What’s New in DV2.0?
© LearnDataVault.com
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Data Vault Model Flexibility (Agility)
Goes beyond standard 3NF• Hyper normalized
● Hubs and Links only hold keys and meta data● Satellites split by rate of change and/or source
• Enables Agile data modeling● Easy to add to model without having to change existing structures
and load routines• Relationships (links) can be dropped and created on-demand.
● No more reloading history because of a missed requirement
Based on natural business keys• Not system surrogate keys• Allows for integrating data across functions and source
systems more easily● All data relationships are key driven.
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Data Vault Extensibility
Adding new components to the EDW has NEAR ZERO impact to:• Existing Loading
Processes• Existing Data Model• Existing Reporting & BI
Functions• Existing Source Systems• Existing Star Schemas
and Data Marts
(C) LearnDataVault.com #OUGF14
● Standardized modeling rules• Highly repeatable and learnable modeling technique• Can standardize load routines
● Delta Driven process● Re-startable, consistent loading patterns.
• Can standardize extract routines● Rapid build of new or revised Data Marts
• Can be automated‣ Can use a BI-meta layer to virtualize the reporting
structures‣ Example: OBIEE Business Model and Mapping tool‣ Example: BOBJ Universe Business Layer
‣ Can put views on the DV structures as well‣ Simulate ODS/3NF or Star Schemas
Data Vault Productivity
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• The Data Vault holds granular historical relationships.
• Holds all history for all time, allowing any source system feeds to be reconstructed on-demand
• Easy generation of Audit Trails for data lineage and compliance.
• Data Mining can discover new relationships between elements
• Patterns of change emerge from the historical pictures and linkages.
• The Data Vault can be accessed by power-users
Data Vault Adaptability
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Other Benefits of a Data Vault
Modeling it as a DV forces integration of the Business Keys upfront.• Good for organizational alignment.
An integrated data set with raw data extends it’s value beyond BI:• Source for data quality projects• Source for master data • Source for data mining • Source for Data as a Service (DaaS) in an SOA (Service Oriented Architecture).
Upfront Hub integration simplifies the data integration routines required to load data marts.
• Helps divide the work a bit.
It is much easier to implement security on these granular pieces. Granular, re-startable processes enable pin-point failure correction. It is designed and optimized for real-time loading in its core
architecture (without any tweaks or mods).
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Worlds Smallest Data Vault
The Data Vault doesn’t have to be “BIG”.
An Data Vault can be built incrementally.
Reverse engineering one component of the existing models is not uncommon.
Building one part of the Data Vault, then changing the marts to feed from that vault is a best practice.
The smallest Enterprise Data Warehouse consists of two tables: ● One Hub, ● One Satellite
Hub_Cust_Seq_ID
Hub_Cust_Num
Hub_Cust_Load_DTS
Hub_Cust_Rec_Src
Hub Customer
Hub_Cust_Seq_ID
Sat_Cust_Load_DTS
Sat_Cust_Load_End_DTS
Sat_Cust_Name
Sat_Cust_Rec_Src
Satellite Customer Name
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Notably…
In 2008 Bill Inmon stated that the “Data Vault is the optimal approach for modeling the EDW in the DW2.0 framework.” (DW2.0)
The number of Data Vault users in the US surpassed 500 in 2010 and grows rapidly (http://danlinstedt.com/about/dv-customers/)
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Organizations using Data Vault
WebMD Health Services Anthem Blue-Cross Blue Shield MD Anderson Cancer Center Denver Public Schools Independent Purchasing Cooperative (IPC, Miami)
• Owner of Subway
Kaplan US Defense Department Colorado Springs Utilities State Court of Wyoming Federal Express US Dept. Of Agriculture
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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
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This model is fully compliant with Hadoop, needs NO changes to work properly
RISK: Key Collision
What’s New in DV2.0?
© LearnDataVault.com
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Summary
• Data Vault provides a data modeling technique that allows:
‣ Model Agility‣ Enabling rapid changes and additions
‣ Productivity‣ Enabling low complexity systems with high
value output at a rapid pace‣ Easy projections of dimensional models
‣ So? Agile Data Warehousing?
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Super Charge Your Data Warehouse
Available on Amazon.comSoft Cover or Kindle Format
Now also available in PDF at LearnDataVault.com
Hint: Kent is the Technical Editor
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Data Vault References
www.learndatavault.comwww.danlinstedt.com
On YouTube: www.youtube.com/LearnDataVault
On Facebook: www.facebook.com/learndatavault
Contact Information
Kent GrazianoThe Oracle Data Warrior
Data Warrior [email protected]
Visit my blog at
http://kentgraziano.com