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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
The “Yellowphant” is blowing it all away!
DWH Modernization09.09.2016
Hadooooop!!
Traditional
BI & DWH
2
The future belongs to data
4th Industrial Revolution
Digital Transformation
Traditional IT
09.09.2016 DWH Modernization3
DWH Modernization09.09.2016
Traditional
Business
Intelligence
DW
Moderni-
zation
Big Data &
Data Science
IoT
EVERYTHING AS A SERVICE
CLOUD COMPUTING
4
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
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, …
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
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)
Scope Modernization of BI/DWH Modernization
DWH Modernization9 09.09.2016
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
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
„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
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
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 .
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
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 .
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
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, …
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
World of 2 velocities
DWH Modernization20 09.09.2016
Traditional BI/DWH Big Data & Data Science
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
This and other Questions…
DWH Modernization22 09.09.2016
Fragen und Antworten …
Gregor Zeiler
Senior Solution Manager
gregor.zeiler@trivadis.com
09.09.2016 DWH Modernization23

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Trivadis TechEvent 2016 DWH Modernization – in the Age of Big Data by Gregor Zeiler

  • 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