Data Vault automation conference - all presentations

Content accountability

Ronald Damhof

Tom Breur

Organization accountability

Simone Molenaar

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Prudenza !

To Push or To Pull, That is the question

Ronald Damhof

woensdag 21 september 11 1

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Push characteristics

! Mass production

! Known specifications, operational definitions, standards

! Repeatable, predictable, & even better; uniform process

! Part of the system that needs statistical control

! Inventory allowed/necessary

! Supply driven

! Reliability over flexibility

Pull characteristics

! Just in time

! Demand driven

! Build to order

! Preferably no inventory

! Flexibility over Reliability


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Remember the Push characteristics
! Mass production Data Vault

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! Repeatable, predictable, & even better; uniform process Data Vault

! Part of the system that needs statistical control Data Vault

! Inventory allowed/necessary Data Vault

! Mainly supply driven Data Vault

! Reliability over flexibility Data Vault

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Qosqo !

Introducing QUIPU

October 2011

Jeroen Klep
QOSQO

+31 6 2953 2342
Jeroen.Klep@QOSQO.nl

open source data warehousing

Agenda

New
Background Architecture
developments

What is a quipu?

AD 1300 - 1600

•  BI strategy development •  Maintenance & support
•  Information analysis •  Data vault technology
•  (E)DW architecture •  Quipu development
•  Project management
•  Adapttm training

QUIPU: Open Source DW generation
•  Open Source Data Warehouse Generation System, based on
Data Vault principles
•  First public release July 1st 2010
•  QOSQO takes a leading role in continuous development and
support

Fast implementation
of DV based EDWH

Removal of
repetitive tasks

Reduction of risk
of modeling errors

Source:

QUIPU - Key IT benefits
•  Automated data warehouse data model design
and implementation
•  Fully repository based metadata driven data
model and load code generation
•  Supports most common database platforms using
ANSI-SQL over JDBC
–  Template based platform support
•  Integration with ETL and scheduling tools
•  Lower total cost of ownership using open source
licensing model

Characteristics

Design time

Run time
Source(s) Target DW

Quipu basic architecture: ‘classic’

Business model
•  Development of new functionality
–  Paid customer assignments
–  QOSQO roadmap priority
•  Support
–  Quick start consultancy
–  Proof of Concepts
–  Flexible support model
•  On site
•  Remote
•  Training
•  Quipu Model Manager
–  Paid software
–  Hosting

Quipu products
Community Model
Edition Manager Powered by

New DWH Management &
developments Maintenance

-  Open source -  Closed source -  Embedded in BI
-  Generate models -  Manage models solutions
-  Single user -  Delta changes
-  Continuous -  Multi-user -  CaseWise Modeler
developments and solution
improvements

-  New: Data mart -  New
New product -  New solutions
DM product
generation roadmap
generation roadmap
assistance

Data Mart assistance
•  In cooperation with BinckBank
•  Logical layer on top of DataVault
•  Basic Starschema or snowflake generation

Quipu Model Manager
•  Version control of data models
•  Multiple users, projects, versions
•  Quipu Community Edition as client
•  Check in / Check out
•  Migration of run time DW data
•  Central repository of models and code
Quipu
CE
Quipu
Quipu MM
CE

Quipu
CE

•  Download and evaluate Quipu (it’s free!)

•  Share your experience and feature wishes

•  Hire us

More info
•  www.datawarehousemanagement.org
•  @OS_Quipu
•  Demo Youtube channel: ‘osquipu’
•  Sourceforge: https://sourceforge.net/projects/quipu/

•  www.QOSQO.nl

QOSQO, the DataVault Our sister company
Karel Doormanlaan 1b
specialist, is the leading Nippur assists in
5688 BP OIRSCHOT company behind Quipu executing business
The Netherlands intelligence projects
E: info@QOSQO.nl
T: +31 ( 0499 ) 577 562 www.QOSQO.nl www.nippur.nl
F: +31 ( 0499 ) 577 059

open source data warehousing

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Logica

29 september 2011

Metadata driven Data Integration
Hype or reality ?

Datavault Conference - Automation
Bertram Hof & Tom van Gessel
6-10-2011

Generating or still Programming

!  Do you use Data Integration tools ?

!  Do you use Metadata Exchange ?

!  Do you use design patterns / reusable components

!  Do you spend much time testing

!  Do you have metadata management in place ?

© Logica 2011. All rights reserved Datavault Conference – Automation 6 oct 2011 No. 2

Title of Presentation 1

29 september 2011

Agenda

!  Logica and our BI Practice
!  Framework approach
!  Best practices
!  Demo Mapping Builder
!  One step beyond, Business Metadata driven
!  Recap
!  Q & A


Logica, our presence - Europe

" Our widespread presence
means we have the capability 9,600
to sell and deliver where our UK Nordics
clients work and live
5,400
" Speaking the same language 1,900
Germany
gives us strong client and
5,500
cultural intimacy Benelux

200
" Combining these skills with
blended delivery is a platform 8,900 CEE
France
to deliver services in the most
efficient way to our clients 900
Portugal



29 september 2011

Logica, our BI workforce world wide

> 3000 consultants work on BI every day,
on site, remote, near- & offshore


in
Roll out thought leader in
Logica, plan
ce
en
Business Intellig w
Europe, launches its ne
book to share its vision
How to Transform Information
Into a Competitive Asset
Discover the BI Framework

Investing in Business Intelligence to aid
competitiveness is, for the fourth year in a row,
top priority for CIOs, say analysts. BI is even
more important when times are tough: it can
help find bottlenecks and inefficiencies or
expose areas that are profitable.

Knowing that most organisations already have some
BI solutions in place, this publication focuses on cost
effective management of BI and provides with a clear
roadmap on how to lower the total cost of
ownership of the current landscape.

Discover a structured approach to manage the
BI life cycle in a cost effective and efficient
manner.



29 september 2011

Agenda

!  Best practices
!  Recap
!  Q & A


Logica - BI Framework
Business Focus
ICT Focus

Operation Focus Change Focus



29 september 2011

Logica - BI Referentie architectuur

Operational Actionable
Data Information

Client
Operations
Services
Reporting PDA

Product X
Sales
Services RSS
Enterprise Analytics
Data
Product Y Warehouse Finance Mail
Services

Mining
Product Z Web
Marketing
Services
Extract Access Publish
Source Integrate Storage Subject Area Utilities Personalise
Present

Data Warehouse (back-end) Business Intelligence (front-end)
Sequential Development Iterative Development


Logica - Engineering Framework
BI Engineering Framework subject models
Deliverable
Data Function Network Timing People Motivation
Mission & Vision statement
Services
Business Business Business Organisational Goals &
&
Terms Locations Events Entities Strategy
Business Products
Context Semantic Business
Logistic Master Organisational Objectives
data process
System Plan Structure & Policies
model model
Enterprise Architecture criteria, topologies and standards
BI
BI BI
System semantic BI infra BI event BI user task
essential semantic
Context data context model model
context rule model
model
BI
BI architecture criteria, topologies and standards
Architecture
Logical Logical Logical
System Logical Logical user Logical
data process control
Concept Infra. Model interface mdl. rule model
model model model
Physical Physical Physical
System Physical Physical user Physical
d ata process control
Specification Infra. Model interface mdl. rule model
model model model
Busines
Repository Database Process Infrastructure Procesflow User interface
rule
data & Code code code Environments code code
code
Business
BI Solution Database Process Infrastructure Procesflow User interface
rule
Configuration objects objects Environments objects objects
objects



29 september 2011

Agenda

!  Best practices
!  Recap
!  Q & A


DWH, layer reference model

" 3nf " Per source " Per source " Per source " Integrated " Integrated " Subject
" incomplete " Source " Source " Storage " Target " Subject oriented
history model model model " Delta oriented " Business
" Detail " Delta " Complete " Complete " Truncate/ " Dimensional Language
" OLTP " Truncate/ History History insert Model
insert " Merge " Merge " Merge

Source IMP STG/ODS DVT
1 1 1 1 D D D D

Source IMP STG/ODS DVT F F
2 2 2 2

D D D D
Bron n
Source IMP STG/ODS DVT
n n n n

IMP STG/ODS DVT INT/BVT STO/DMT

Source Knowledge
system Reference data
Worker

Meta data

Processflow



29 september 2011

Productivity Boosters

!  Import flat files – Import Tabel + Import Mapping

!  Staging / ODS – Staging/ODS tabel + Merge / SC Mapping

!  Storage / Datamart – Dimension / Fact + Mapping

!  Processflows

!  Quantitative Measures

!  Seedfile driven generation – flatfiles / imp /ods

!  Seedfile driven generation – XML delivery / interfaces

!  Seedfile driven generation – dimension/fact loading

!  Datavault Experts – Hup, Link en Satellite generation


Example of Productivity Booster Datavault Link



29 september 2011

Some Practical results

!  Seedfile driven approach ODS => 15-30% of budget

!  Productivity Boosters during development => 10-20% of budget

!  Quality improvement => 40%

!  Test reduction => 70%

!  Exploitation reduction

!  Time to market

!  Impact Analysis

! …


Agenda

"  Logica and our BI Practice
"  Framework approach
"  Best practices
"  Demo Mapping Builder
"  One step beyond, Business Metadata driven
"  Recap
"  Q & A



29 september 2011

Demo, mapgen the functionality

Mapping generation with informatica powercenter

Parameters

Repository
Mapgen Informatica

Visio
Template


Demo, mapgen the templates

Target ODS

Target Satellite (DataVault)



29 september 2011

Demo Mapping Builder

!  Ferarri case


Agenda

!  Best practices
!  Recap
!  Q & A



29 september 2011

ETL Development Process


ETL Framework a different perspective



29 september 2011

Logica - ETL Framework, components


ETL Generator



29 september 2011

Some lab implementations

Microsoft
SSIS

IBM Cognos/
Infosphere


Lab results

•  BI- Platform indepedant ETL methode
•  Generic ETL model/design

•  Cost reduction of 8% with ETL Framework

•  Cost reduction of 17% with ETL Generator

•  Combination of ETL Framework and ETL Generator will
result in cost reduction > 26%



29 september 2011

Agenda

!  Best practices
!  Recap
! Q & A


Recap

Generation of Dataware House not a hype but reality

"  Main parts of the datawarehouse can be generated

"  Requirement Capture needs further maturity
"  Framework approach provides the structures needed to generate
"  Mature enough to use within projects and organisations
"  Quality results obvious /Testtime reduction
"  Faster implementation, time to market / Reduced TCO



29 september 2011

Agenda

!  Best practices
!  Recap
! Q & A


Q&A



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XLNTconsulting !

Agile BI:
Accounting for progress

Tom Breur
Data Vault Automation
Utrecht, 6 Oktober 2011

“Our highest priority is to satisfy the
customer through early and continuous
delivery of valuable software”

Agile Manifesto, 2001
Kent Beck, Mike Beedle, Arie van Bennekum, Alistair Cockburn, Ward Cunningham,
Martin Fowler, James Grenning, Jim Highsmith, Andrew Hunt, Ron Jeffries, Jon Kern,
Brian Marick, Robert C. Martin, Steve Mellor, Ken Schwaber, Jeff Sutherland, Dave Thomas

www.xlntconsulting.com 2

Counter intuitive Agile practices
! People are more productive if nobody tells
them what to do
! Pair programming leads to more (effective)
production code
! Business partners must be full-time
engaged (co-located) with the
development team


Counter intuitive Agile practices
! Only the business has the right to choose
what gets done
! An efficient team must have “slack”, must
have people sitting idle, with nothing
productive to do, on a regular basis
! Etc.


Software ‘inventory’

“Work-in-Progress is a liability
– not an asset”
Tom Breur, 2011


Simplified development

Error Reports

Idea Develop Test Working Code


(More) realistic development

Idea Analysis Design Code

Error Error Error

Working Acceptance System Unit
Code Test Test Test


Agile manufacturing

Theory Focus
J-i-T Inventory
TQM/QA Quality & Conformance
T-o-C Bottlenecks
Lean Inventory, Quality &
Conformance
Six Sigma Quality & Variance


Throughput Accounting metrics
THROUGHPUT INVENTORY

Rate of cash* generated through Quantity of ideas for client-valued
delivery of working code into functionality queing for input to, in-
production, not merely code process through, or waiting for
complete output, from the system

*Assuming a constant level of Investment

INVESTMENT OPERATIONAL EXPENSE

The sum of money invested in the The sum of money spent in the
system of software production plus system to produce working code from
the sum spent to obtain the ideas for ideas for client-valued functionality
client-valued functionality input to the (marginal expense to create
system (gathering requirements) production code)


ROI in Throughput Accounting

Unknown (T) – Pretty hard to guess (OE)
ROI =
Didn’t bother to measure (I)


NP in Throughput Accounting

(more) Net Profit (NP) = T – (less) OE



Throughput (T) – Operating Expense (OE)
(more) ROI =
(less) Investment in Inventory



(more) Net Profit (NP) = (more) T - OE

(more) Throughput (T) – Operating Expense (OE)
(more) ROI =
Investment


Focus on Throughput
! Focus on T, I, or OE?
! Throughput is unlimited, it can grow
forever
! Focusing on cost has a logical (yet
unattainable) lower bound – namely zero
! Throughput focuses on the customer –
externally
! Cost focuses on the team – internally

Investment
! Minimizing Investment (I) drives ROI up
! Minimizing Investment also reduces OE,
by reducing carrying cost of capital
! And, most importantly
! Lower I means lower inventory,
which leads to reduced Lead Times,
hence earlier delivery of value
(Agile Manifesto principle #1)


Cost vs Throughput Accounting
Cost Accounting Throughput Accounting
!  Inventory is an asset !  Inventory is a liability
!  Efficiency = function/ !  Efficiency = function/
dollar (hours) " labor is direct costs (idle or not)
a “variable” cost " labor is a “fixed” cost
!  People sitting idle are !  People sitting idle are a
discarded! part of the system!


Cost vs Throughput Accounting

Cost Accounting

Operating
Inventory Production

Least Focus
Expense
Most Focus

Throughput Operating
Inventory
(Production) Expense

Throughput Accounting


Agile & Data Vault
! (very) few other architectures allow
incremental build at such low marginal
cost
! Deliver early – in (very) small increments
! (very) few other architectures allow
‘mistakes’ in your model, that you can
recover from inexpensively
! Deliver early – (long) before you have settled
on “the” final business model

Conclusion
! By providing appropriate metrics
(=Throughput Accounting), complex
adaptive systems (Agile projects) will
display the desired emergent properties
! Agile BI is not about delivering faster (or
cheaper) – efficiency
! Agile BI is about delivering in arbitrarily
smaller increments to end-users – hence
gathering feedback about effectiveness

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Genesee Academy !

25568 Genesee Trail Rd
Golden, Colorado 80401
(303) 526-0340

Data Vault Modeling and Approach DW2.0 and Unstructured Data Master Data Management and Metadata

Data Vault
DW Automation
Classification Matrix

Data Vault Automation Conference 2011

©2011 Genesee Academy, LLC

25568 Genesee Trail Rd
Golden, Colorado 80401
Hans Hultgren

© 2011 Genesee Academy, LLC

Welcome

Overview of Data Warehouse Automation
Scope of the Classification Matrix
Classification Criteria
Automation Categories
The Automation Matrix
Applying the Matrix



Operational Applications support business processes.
Typically this implies the support and partial
automation of components of a particular business
process.
In addition to software, business processes are also
supported by methodologies, frameworks, specialized
techniques, and also forms, templates and checklists.
Together these form a pool of tools and techniques that
support certain aspects of these business processes.



With Data Warehousing and Business Intelligence
another pool of tools and techniques exists to support
particular aspects of these programs.
In fact these tools and techniques are vast and varied
each addressing some combination of DWBI activities.
To limit these tools to some degree, this presentation
will focus mainly on Enterprise Data Warehousing and
in particular those that utilize data vault modeling.


Scope of the Classification Matrix

As mentioned, the focus is on Enterprise Data
Warehousing:
* Integrated * Non-‐Volatile * Time-‐Variant
* Subject Oriented * Auditable * Adaptable

* Atomic Level * All Data * Traceable
* Business Key Based * Business Aligned * Hub/Link/Sat
Plus all forms of automation tools and techniques:
* Software Tools * Methodologies * Frameworks
* Code Generators * Templates * Shells
* Common Models * Documentation * Metadata



To begin working with the automation matrix we must
consider and understand the various classification
criteria.
Effectively (in simple terms) this means that we look at
different ways of thinking about these tools and
techniques.
As you will find, when a certain classification criteria is
presented, and you begin to think about that criteria in
context, the meaning becomes clear.



For example, consider the following classification
criteria:
Templates for ETL
Support for Data Modeling
Generation of Mappings
Automation of Testing
For each one, consider them in the context of some of
the tools and techniques presented earlier today.
This process of contemplating criteria in the context of
particular tools and techniques is the purpose of the
automation matrix

Data Vault automation conference - all presentations

Data Vault automation conference - all presentations

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