1.
BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF
HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH
DWH Modernization –
in the Age of Big Data
Gregor Zeiler
Senior Solution Manager BI/Big Data
@GregorZeiler

2.
The “Yellowphant” is blowing it all away!
DWH Modernization09.09.2016
Hadooooop!!
Traditional
BI & DWH
2

3.
The future belongs to data
4th Industrial Revolution
Digital Transformation
Traditional IT
09.09.2016 DWH Modernization3

4.
DWH Modernization09.09.2016
Traditional
Business
Intelligence
DW
Moderni-
zation
Big Data &
Data Science
IoT
EVERYTHING AS A SERVICE
CLOUD COMPUTING
4

5.
DWH Modernization09.09.2016
Traditional
Business
Intelligence
DW
Moderni-
zation
Big Data &
Data Science
IoT
EVERYTHING AS A SERVICE
CLOUD COMPUTING
Traditional Business Digital Business
5

6.
Technical Driver
Leading Driver for DW Modernization
DWH Modernization09.09.2016
Source: Data Warehouse Modernization, Best Practices Report, Q2/2016
6
Business Driver
Business alignment
Modern practices for
Analytics, …
Data Lake,
Data Vault, Hadoop, …

7.
Expectations on modernized DW-Solutions
DWH Modernization09.09.2016
Business
Source: Data Warehouse Modernization, Best Practices Report, Q2/2016
Analytics, Exploration,
Better decision making
Operational efficiency
Technology
Agility,
Maintenance,
Automation
7

8.
Typical Starting Position for Modernization
DWH Modernization8 09.09.2016
DM
Marketin
g
Kern-DWH
Staging Area
DM
Vertrieb
neu
DWH Post Merger Datenpool
Ad-hoc
DM
Bestand
DM
Abschlus
s
DM
Vers.tec
h.
DM
Finanzen
DM
Anpassu
ng
OLAP
Finanzen
OLAP
Vertrieb
DM
Vertrieb
Vertriebs-
Reporting
Reporting
Finanzen
OLAPVertrieb-Ad-
hoc-Analysen
OLAP
Marketing
Finanzen-Ad-
hoc-Analysen
Meldewesen Datenextrakte
(PE., Bilanz,
etc.)
Over several years grown Data Warehouse Solution which does not meet both current and
upcoming requirements (66% DW Projects have started before 10 years)
Source: Data Warehouse Modernisierung – Auslöser, Stoßrichtungen und Potenziale (Erik Purwins, Gregor Zeiler)

9.
Scope Modernization of BI/DWH Modernization
DWH Modernization9 09.09.2016

10.
Modern Analytical Data Management Solutions (DWE)
DWH Modernization10 09.09.2016
Data Access
Metadata, Governance, Data QualityGovernance, Data Quality
Meta
Data
Service Decision Layer
RELIABLE
DATABASE
FEXIBILITY
AGILITY
VARIABILITY
ROW-DATA
„Classic“
DWH
Federation
Virtualization
Big Data
Analytics
The traditional data warehouse has been overwhelmed by analytic demands, giving rise to the
logical data warehouse as a best-in-class architecture for a new style of data management
solutions for analytics. © Gartner: 2014, Analyst(s): Mark A. Beyer, Roxane Edjlali

11.
Landscape for Analytical Data Management Solutions
DWH Modernization11 09.09.2016
Data
Acquisition
Data
Sources
Governance
Organisation
Information
Provisioning Consumer
Data
Management
Legal ComplianceQuality & Accountability SecurityMetadata Management Master Data Management
IT Operations Business StakeholdersBI Competence Center
Un-/Semi-
structured Data
Structured
Data
Master & Reference
Data
Machine Data
Content
Services(Push)Connectors(Pull)
StreamBatch/Bulk
IncrementalFull
Raw Data at Rest
Standardized Data at Rest
Optimized Data at Rest
Data Lab (Sandbox)
Data Refinery/Factory
Virtualization
Raw Data in Motion
Standardized Data in Motion
Optimized Data in Motion
Query
Service / API
Search
Information
Services
Data Science
Tools
Dashboard
Prebuild &
AdHoc BI Assets
Advanced Analysis
Tools

12.
„Classical DWH“ based on analytical Landscape
DWH Modernization12 09.09.2016
Data
Acquisition
Data
Sources
Governance
Organisation
Information
Provisioning Consumer
Data
Management
Legal ComplianceQuality & Accountability SecurityMetadata Management Master Data Management
IT Operations Business StakeholdersBI Competence Center
Un-/Semi-
structured Data
Structured
Data
Master &
Reference
Data
Machine Data
Content
Services(Push)Connectors(Pull)
StreamBatch/Bulk
IncrementalFull
Raw Data at Rest
Standardized Data at Rest
Optimized Data at Rest
Data Lab (Sandbox)
Data Refinery/Factory
Virtualization
Raw Data in Motion
Standardized Data in Motion
Optimized Data in Motion
Query
Service / API
Search
Information
Services
Data Science
Tools
Dashboard
Prebuild &
AdHoc BI Assets
Advanced Analysis
Tools
Core DWH
Data Marts
Staging Area
ETL

13.
Data
Acquisition
Data
Sources
Governance
Organisation
Information
Provisioning Consumer
Data
Management
Streaming Data based on analytical Landscape
DWH Modernization13 09.09.2016
Legal ComplianceQuality & Accountability SecurityMetadata Management Master Data Management
IT Operations Business StakeholdersBI Competence Center
Un-/Semi-
structured Data
Structured
Data
Master &
Reference
Data
Machine Data
Content
Services(Push)Connectors(Pull)
StreamBatch/Bulk
IncrementalFull
Raw Data at Rest
Standardized Data at Rest
Optimized Data at Rest
Data Lab (Sandbox)
Data Refinery/Factory
Merge Layer
Raw Data in Motion
Standardized Data in Motion
Optimized Data in Motion
Query
Service / API
Search
Information
Services
Data Science
Tools
Dashboard
Prebuild &
AdHoc BI Assets
Advanced
Analysis Tools
Event Hub
Stream Analytics
Hadoop Raw Data
Processed Files
NoSQL DB
SQL Engine

14.
BI/DWH Strategy for the next 3 years
DWH Modernization14 09.09.2016
decrease
increase
Quelle: Data Warehouse Modernization, Best Practices Report, Q2/2016, Philip Russom .

15.
BI/DWH Strategy for the next 3 years
DWH Modernization15 09.09.2016
Quelle: Data Warehouse Modernization, Best Practices Report, Q2/2016, Philip Russom .
50% combine
6% replace

16.
BI/DWH Strategy for the next 3 years
DWH Modernization16 09.09.2016
57% replace
Quelle: Data Warehouse Modernization, Best Practices Report, Q2/2016, Philip Russom .

17.
Greenfield
Status Quo
R 
E 
B 
Feature Extension
R 
E 
B 
Partial renewal
Modernization
R 
E 
B 
Functional
Modernization
R 
E 
B 
Possible Modernization Strategies
DWH Modernization17 09.09.2016
44%
Disruptive Modernization
R 
A 
B 
Performance Opt.
R 
E 
B 
Re-
Platforming
21%
42% 47%
Data Lab
Data Lake,
Hadoop
Source of information:
tdwi Best Practices Report Q2/2016
DWH Modernization
..%
Percentage of selected
Modernization Strategy.
Multiple Choices possible.
…
Sample Modernization
Approaches
Legende:
R…Risc
E…Effort
B…Benefit
…high
…medium
…low
RenewalReengineering Replacement
ExtensionStatusQuoExtended
Data
Vault
System Mod.
R 
E 
B 
53%
42% with
Extension-
Strategy
58% with
Renewal-
Strategy

18.
Architectures
Features Technology
Scalability
Automation
DataOrganization
Expenses
Cloud, Services,
Elastic-DWH …
Data Vault (DW Core)
EDWH  LDW
Big Data …
Advanced Analytics,
Self-Service, …
Hadoop, NoSQL
InMemory…
Development-, Change-
Automation …
New Data-sources/-types
Quality/Security…
Governance, Compliance
Processes, Skills…
Dev./Operating
CAPEX/OPEX…
BI/DWH Modernization areas
DWH Modernization18 09.09.2016
DWE
DW
EDWH, Silos, …

19.
Route to modern Data Warehouse Environments
DWH Modernization19 09.09.2016
DWE Vision
Target Solution
Roadmap to Vision
Existing Problems
and Pain Points
Upcoming
Requirements
Modern Analytical
Architectures
Existing DWH Solution

20.
World of 2 velocities
DWH Modernization20 09.09.2016
Traditional BI/DWH Big Data & Data Science

21.
Conclusion
DWH Modernization21 09.09.2016
Digital Business drives DWH Modernization
Enhance the scope to Data Warehouse Environments
Design your Architecture by Pains and Needs not
primarily by Technology
Choose a suitable Modernization Strategy - be spunky
Be aware of the two velocities

22.
This and other Questions…
DWH Modernization22 09.09.2016

23.
Fragen und Antworten …
Gregor Zeiler
Senior Solution Manager
gregor.zeiler@trivadis.com
09.09.2016 DWH Modernization23