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Extended Data Warehouse -
A New Data Architecture
for Modern BI
Today’s Speakers
■ Paul Moxon
Senior Director, Product Management
Denodo Technologies
■ Claudia Imhoff
President, Intelligent Solutions
Founder, Boulder BI Brain Trust
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Agenda
 Extending the Data Warehouse Architecture
 Use Cases for a Modern BI Environment
 Things to Ponder…
 XDW – Real World Examples
3
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Next Generation BI
4Based on a concept by Shree Dandekar of Dell
Business
insights
Economics
New
technologies
Non-traditional
data sources
Increasing
data volumes
& data rates
Extended data
warehouse
Next
generation
BI
DRIVERS
FEATURES
Slide compliments of Colin White – BI Research, Inc.
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
A Complex BI Environment
5
Multiple user devices
Multiple output formats
Multiple deployment options
Sophisticated analytics
+ complex analytic workloadsMultiple data sources
Increasing data volumes
& data rates
DW historical
data
Web & social
content
Sensor
data
Operational
data
Text &
media files
Decision
management
Data
management
Data
integration
Data
analysis
Decision
management
Slide compliments of Colin White – BI Research, Inc.
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
The Extended Data Warehouse
Architecture (XDW)
6
Traditional EDW
environment
Investigative computing
platform
Analytic tools & applications
Other internal & external
structured & multi-structured data
Real-time streaming data
Courtesy of Colin White – BI Research, Inc.Operational real-time environment
RT analysis engineOperational systems
BI services
Data
refinery
Data integration
platform
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Agenda
 Extending the Data Warehouse Architecture
 Use Cases for a Modern BI Environment
 Things to Ponder…
 XDW – Real World Examples
7
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Operational Analytics Use
Case
Embedded or callable BI
services:
 Real-time fraud detection
 Real-time loan risk assessment
 Optimizing online promotions
 Location-based offers
 Contact center optimization
 Supply chain optimization
Real-time analysis engine:
 Traffic flow optimization
 Web event analysis
 Natural resource exploration
analysis
 Stock trading analysis
 Risk analysis
 Correlation of unrelated data
streams (e.g., weather effects on
product sales)
8
Operational real-time environment
RT analysis engine
Other internal & external
structured & multi-structured data
Real-time streaming data
Operational systems
BI services
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Data Provisioning Use Case:
Data Integration
9
 Heavy lifting process of extracting,
transforming to standard format
and loading structured data –
mostly batch
 Physically consolidates data into
“trusted” EDW sets for analysis
 Invokes data quality processing
where needed
 Employs low-cost hardware and
software to enable large data
volumes to be combined and stored
 Requires more formal governance
policies to manage data security,
privacy, quality, archiving and
destruction
Traditional EDW
environment
Investigative computing
platform
Data
refinery
Data integration
platform
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Data Integration Cases
 For use with production analyses in the traditional
enterprise data warehouse
 Data is consolidated into higher quality, trusted sets
 Trickle feeds allow near real-time analytics
 Reliable, consistent, historical data for production reporting, multi-
dimensional analytics, advanced analytics
 Probably is part of formal data governance process
 Is conducted in persistent staging area
10
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Data Provisioning Use Case:
Data Refinery
11
 Ingests raw detailed structured and
unstructured data in batch and/or
real-time into a managed data store
 Distills data into useful business
information and distributes the
results to downstream systems
 May also directly analyze certain
types of data
 Also employs low-cost hardware
and software to enable large
amounts of detailed data to be
managed cost effectively
 Requires (flexible) governance
policies to manage data security,
privacy, quality, archiving and
destruction
Traditional EDW
environment
Investigative computing
platform
Data
refinery
Data integration
platform
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Data Refinery Cases
 Many organizations use the data refinery to determine
what’s of value in big data
 Not all data is useful
 Quickly discover interesting data
 Perform rough analyses to determine valuable data
 Move valuable data only into the investigative computing platform
or to the data integration platform
 Probably not part of formal data governance process
 Can be considered part of the staging area
12
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Traditional EDW Use Cases
13
Most BI environments today
 New technologies can be
incorporated into the EDW
environment to improve
performance, efficiency & reduce
costs
Use cases
 Production reporting
 Historical comparisons
 Customer analysis (next best offer,
segmentation,
life-time value scores,
churn analysis, etc.)
 KPI calculations
 Profitability analysis
 Forecasting
Traditional EDW
environment
Data
refinery
Data integration
platform
Analytic tools & applications
Operational real-time environment
RT analysis engineOperational systems
BI services
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Investigative Computing Use
Cases
New technologies used here
include:
 Hadoop, in-memory computing,
columnar storage, data
compression, appliances, etc.
Use cases
 Data mining and predictive
modeling for EDW and real-
time environments
 Cause and effect analysis
 Data exploration (“Did this ever
happen?” “How often?”)
 Pattern analysis
 General, unplanned
investigations of data
14
Data
refinery
Data integration
platform
Analytic tools & applications
Operational real-time environment
RT analysis engine
Investigative computing
platform
Operational systems
BI services
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
All Components Must Work Together
Data Virtualization is Mandatory
15
analytic models
analyses
New sources of data Enterprise DW
Analytic tools
Investigative
computing platform
Data refinery Operational systems
existing
customer
data
next best
customer offer
3rd party data
location data
social data
feedback
RT analysis engine
call center dashboard
or web event stream
Slide created by Colin White – BI Research, Inc.
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Need for Analytics
 Definition:
 Practice of iterative, methodical exploration of an organization’s
data with emphasis on [advanced] analytical techniques
 Business analytics are used by organizations committed to data-
driven decision-making
 Need:
 Analytics give us far more value from our data than simple
reporting or comparative diagnostics
 They are the only meaningful way to measure success or failure
 They give us more than just descriptions of what happened – why
did it happen, will it continue to happen, what should I do to either
stop it or continue the activity?
16
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Four Forms of BI
17
Based on Delen, Dursun and Demirkan, Haluk, “Decision Support Systems, Data, information and analytics as services,”
from Elsevier, published online May 29, 2012
Business Analytics
Descriptive
(Reactive)
Prescriptive
(Proactive)
Predictive
(Proactive)
What happened?
What is happening?
•Business reporting
•Dashboards
•Scorecards
•Data warehousing
Well-defined
business problems
and opportunities
What will happen?
•Data mining
•Text mining
•Web/media mining
•Forecasting
Accurate projections
of the future states
and conditions
What should I do?
Why should I do it?
•Optimization
•Simulation
•Decision modeling
•Expert systems
Best possible
business decisions
and transactions
OutcomesEnablersQuestions
Diagnostic
(Reactive)
Why did it happen?
•Behavioral analysis
•Cause and effect
analysis
•Correlations
Cause and effects of
changes in business
activities
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Agenda
 Extending the Data Warehouse Architecture
 Use Cases for a Modern BI Environment
 Things to Ponder
 XDW – Real World Examples
18
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Things to Think About
 Understand advantages and disadvantages of data
virtualization
 Advantages:
 Quick and fast access to any data
 No physical movement of data needed
 Low or no latency in accessing data
 Disadvantages
 Data virtualization does not replace ETL for EDWs
 It can impact performance of operational systems
 If data quality and data transformations are complex (e.g.,
multi-path), data virtualization is not recommended
19
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Things to Think About
 Understand security needs in a virtual world
 Data virtualization can give data architects a “choke point” to
enforce security policies
 Understand your failover and scale-up requirements
 Eliminate rogue or unneeded data marts
 The benefit of data virtualization is the reduced need of physical
instantiations of data
 Create virtual marts as a standard practice unless there is a
compelling reason for a physical one
 Integrate cloud and on-premises sources virtually
 Be sure you can virtualize relational and non-relational
data sources together
20
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Business Is In The Driver’s
Seat
 Self-service BI – used to expand BI throughout the enterprise but…
 IT must be recognized as being important to the business
 A company that puts no thought into information management and
analysis won’t be around for long
 IT is a significant partner and enabler to business strategies
 Business must have healthy relationship with IT professionals – most
important aspect of becoming a data-driven company
 Business must be recognized as technologically-savvy
 Emergence of super-analyst: someone highly
skilled, highly empowered, and highly productive
when set free
 Analysts prefer using their own tools instead of
ones blessed by IT & sanctioned by the
organization
 Virtualization is an important technology here
21
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Future: The Customer is in the
Driver’s Seat!
 Caution! Paradigm shift ahead!
 Customer’s mobile phone becoming their mobile wallet - and their
personal data warehouse
 When customers interact with companies, they get a copy of “their” data
 Shopping information
 Financial information
 Medical information
 Phone / Text information
 Only they have the 360 degree view
of their own data
 Questions
 Can they monetize their information?
 Can they put their needs out to bid?
 Can they virtualize their own data?
22
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Final Thoughts
23
Need fast time to value to gain business benefits from big data technologies
o Impractical to use traditional enterprise DW approach for all solutions
o Need to extend the existing DW environment to support new capabilities
Need high performance solutions for supporting new BI analytic workloads
o One-size fits all data management is no longer viable
o Match technologies and costs to business needs and analytic workloads
Need to modify data modeling and integration approaches
o Need to support new data types, sources and platforms, and new approaches such
as data blending, schema-on-read and data refineries
Need to modify data governance approaches
o No longer practical to rigidly control and govern all forms of data – use different
levels of governance based on security, compliance, quality and retention needs
Slide compliments of Colin White – BI Research, Inc.
Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved
Agenda
 Extending the Data Warehouse Architecture
 Use Cases for a Modern BI Environment
 Things to Ponder…
 XDW – Real World Examples
24
Extended Data Warehouse Architecture
– Real World Examples
Extended Data Warehouse Architecture -
Recap
Traditional EDW
environment
Investigative computing
platform
Analytic tools & applications
Other internal & external
structured & multi-structured data
Real-time streaming data
Slide created by Colin White – BI Research, Inc.
Operational real-time environment
RT analysis engineOperational systems
BI services
Data
refinery
Data integration
platform
Analyzes weather data to provide insurance to farmers who can lock in profits even in
the case of drought, excessive rains or other adverse weather conditions
• Aggregation of very large dynamic data sets
• Mix of cloud, web and internal data
• Excel and Tableau to generate reports on risk assessment, and recommend
coverage/price policies
Data Environment
Data Relationships
Deployment in Amazon EC2 Cloud
Telematics - IoT
Data services access to machine generated data. Business use: predictive
maintenance services
Major Heavy Equipment Manufacturer
Telematics Project
Hadoop Cluster
OSI PI
Dealer
Maintenance
Parts
Inventory
Virtual Views
Dealer/Customer
Dashboards
Summary – Things to Remember
■ The traditional enterprise Data Warehouse
architecture needs to evolve to embrace Big Data
■ However this doesn’t mean that the enterprise
Data Warehouse is not needed
■ Match the technologies and costs to the business
needs
■ You will need to combine data from both the
traditional and new environments
■ Data Virtualization will be an essential component
of the Extended Data Warehouse
Q&A
Data Virtualization – Next Steps
Move forward at your own pace
 Download Denodo Express –
The fastest way to Data Virtualization
 Denodo Community:
Documents, Videos, Tutorials, and more.
Move forward with one of our Data
Virtualization experts
 Phone: (+1) 877-556-2531 (NA)
 Phone: (+44) (0)20 7869 8053 (EMEA)
 Email: info@denodo.com | www.denodo.com
www.denodo.com info@denodo.com

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Extended Data Warehouse - A New Data Architecture for Modern BI with Claudia Imhoff

  • 1. Extended Data Warehouse - A New Data Architecture for Modern BI
  • 2. Today’s Speakers ■ Paul Moxon Senior Director, Product Management Denodo Technologies ■ Claudia Imhoff President, Intelligent Solutions Founder, Boulder BI Brain Trust
  • 3. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Agenda  Extending the Data Warehouse Architecture  Use Cases for a Modern BI Environment  Things to Ponder…  XDW – Real World Examples 3
  • 4. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Next Generation BI 4Based on a concept by Shree Dandekar of Dell Business insights Economics New technologies Non-traditional data sources Increasing data volumes & data rates Extended data warehouse Next generation BI DRIVERS FEATURES Slide compliments of Colin White – BI Research, Inc.
  • 5. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved A Complex BI Environment 5 Multiple user devices Multiple output formats Multiple deployment options Sophisticated analytics + complex analytic workloadsMultiple data sources Increasing data volumes & data rates DW historical data Web & social content Sensor data Operational data Text & media files Decision management Data management Data integration Data analysis Decision management Slide compliments of Colin White – BI Research, Inc.
  • 6. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved The Extended Data Warehouse Architecture (XDW) 6 Traditional EDW environment Investigative computing platform Analytic tools & applications Other internal & external structured & multi-structured data Real-time streaming data Courtesy of Colin White – BI Research, Inc.Operational real-time environment RT analysis engineOperational systems BI services Data refinery Data integration platform
  • 7. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Agenda  Extending the Data Warehouse Architecture  Use Cases for a Modern BI Environment  Things to Ponder…  XDW – Real World Examples 7
  • 8. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Operational Analytics Use Case Embedded or callable BI services:  Real-time fraud detection  Real-time loan risk assessment  Optimizing online promotions  Location-based offers  Contact center optimization  Supply chain optimization Real-time analysis engine:  Traffic flow optimization  Web event analysis  Natural resource exploration analysis  Stock trading analysis  Risk analysis  Correlation of unrelated data streams (e.g., weather effects on product sales) 8 Operational real-time environment RT analysis engine Other internal & external structured & multi-structured data Real-time streaming data Operational systems BI services
  • 9. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Data Provisioning Use Case: Data Integration 9  Heavy lifting process of extracting, transforming to standard format and loading structured data – mostly batch  Physically consolidates data into “trusted” EDW sets for analysis  Invokes data quality processing where needed  Employs low-cost hardware and software to enable large data volumes to be combined and stored  Requires more formal governance policies to manage data security, privacy, quality, archiving and destruction Traditional EDW environment Investigative computing platform Data refinery Data integration platform
  • 10. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Data Integration Cases  For use with production analyses in the traditional enterprise data warehouse  Data is consolidated into higher quality, trusted sets  Trickle feeds allow near real-time analytics  Reliable, consistent, historical data for production reporting, multi- dimensional analytics, advanced analytics  Probably is part of formal data governance process  Is conducted in persistent staging area 10
  • 11. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Data Provisioning Use Case: Data Refinery 11  Ingests raw detailed structured and unstructured data in batch and/or real-time into a managed data store  Distills data into useful business information and distributes the results to downstream systems  May also directly analyze certain types of data  Also employs low-cost hardware and software to enable large amounts of detailed data to be managed cost effectively  Requires (flexible) governance policies to manage data security, privacy, quality, archiving and destruction Traditional EDW environment Investigative computing platform Data refinery Data integration platform
  • 12. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Data Refinery Cases  Many organizations use the data refinery to determine what’s of value in big data  Not all data is useful  Quickly discover interesting data  Perform rough analyses to determine valuable data  Move valuable data only into the investigative computing platform or to the data integration platform  Probably not part of formal data governance process  Can be considered part of the staging area 12
  • 13. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Traditional EDW Use Cases 13 Most BI environments today  New technologies can be incorporated into the EDW environment to improve performance, efficiency & reduce costs Use cases  Production reporting  Historical comparisons  Customer analysis (next best offer, segmentation, life-time value scores, churn analysis, etc.)  KPI calculations  Profitability analysis  Forecasting Traditional EDW environment Data refinery Data integration platform Analytic tools & applications Operational real-time environment RT analysis engineOperational systems BI services
  • 14. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Investigative Computing Use Cases New technologies used here include:  Hadoop, in-memory computing, columnar storage, data compression, appliances, etc. Use cases  Data mining and predictive modeling for EDW and real- time environments  Cause and effect analysis  Data exploration (“Did this ever happen?” “How often?”)  Pattern analysis  General, unplanned investigations of data 14 Data refinery Data integration platform Analytic tools & applications Operational real-time environment RT analysis engine Investigative computing platform Operational systems BI services
  • 15. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved All Components Must Work Together Data Virtualization is Mandatory 15 analytic models analyses New sources of data Enterprise DW Analytic tools Investigative computing platform Data refinery Operational systems existing customer data next best customer offer 3rd party data location data social data feedback RT analysis engine call center dashboard or web event stream Slide created by Colin White – BI Research, Inc.
  • 16. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Need for Analytics  Definition:  Practice of iterative, methodical exploration of an organization’s data with emphasis on [advanced] analytical techniques  Business analytics are used by organizations committed to data- driven decision-making  Need:  Analytics give us far more value from our data than simple reporting or comparative diagnostics  They are the only meaningful way to measure success or failure  They give us more than just descriptions of what happened – why did it happen, will it continue to happen, what should I do to either stop it or continue the activity? 16
  • 17. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Four Forms of BI 17 Based on Delen, Dursun and Demirkan, Haluk, “Decision Support Systems, Data, information and analytics as services,” from Elsevier, published online May 29, 2012 Business Analytics Descriptive (Reactive) Prescriptive (Proactive) Predictive (Proactive) What happened? What is happening? •Business reporting •Dashboards •Scorecards •Data warehousing Well-defined business problems and opportunities What will happen? •Data mining •Text mining •Web/media mining •Forecasting Accurate projections of the future states and conditions What should I do? Why should I do it? •Optimization •Simulation •Decision modeling •Expert systems Best possible business decisions and transactions OutcomesEnablersQuestions Diagnostic (Reactive) Why did it happen? •Behavioral analysis •Cause and effect analysis •Correlations Cause and effects of changes in business activities
  • 18. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Agenda  Extending the Data Warehouse Architecture  Use Cases for a Modern BI Environment  Things to Ponder  XDW – Real World Examples 18
  • 19. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Things to Think About  Understand advantages and disadvantages of data virtualization  Advantages:  Quick and fast access to any data  No physical movement of data needed  Low or no latency in accessing data  Disadvantages  Data virtualization does not replace ETL for EDWs  It can impact performance of operational systems  If data quality and data transformations are complex (e.g., multi-path), data virtualization is not recommended 19
  • 20. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Things to Think About  Understand security needs in a virtual world  Data virtualization can give data architects a “choke point” to enforce security policies  Understand your failover and scale-up requirements  Eliminate rogue or unneeded data marts  The benefit of data virtualization is the reduced need of physical instantiations of data  Create virtual marts as a standard practice unless there is a compelling reason for a physical one  Integrate cloud and on-premises sources virtually  Be sure you can virtualize relational and non-relational data sources together 20
  • 21. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Business Is In The Driver’s Seat  Self-service BI – used to expand BI throughout the enterprise but…  IT must be recognized as being important to the business  A company that puts no thought into information management and analysis won’t be around for long  IT is a significant partner and enabler to business strategies  Business must have healthy relationship with IT professionals – most important aspect of becoming a data-driven company  Business must be recognized as technologically-savvy  Emergence of super-analyst: someone highly skilled, highly empowered, and highly productive when set free  Analysts prefer using their own tools instead of ones blessed by IT & sanctioned by the organization  Virtualization is an important technology here 21
  • 22. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Future: The Customer is in the Driver’s Seat!  Caution! Paradigm shift ahead!  Customer’s mobile phone becoming their mobile wallet - and their personal data warehouse  When customers interact with companies, they get a copy of “their” data  Shopping information  Financial information  Medical information  Phone / Text information  Only they have the 360 degree view of their own data  Questions  Can they monetize their information?  Can they put their needs out to bid?  Can they virtualize their own data? 22
  • 23. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Final Thoughts 23 Need fast time to value to gain business benefits from big data technologies o Impractical to use traditional enterprise DW approach for all solutions o Need to extend the existing DW environment to support new capabilities Need high performance solutions for supporting new BI analytic workloads o One-size fits all data management is no longer viable o Match technologies and costs to business needs and analytic workloads Need to modify data modeling and integration approaches o Need to support new data types, sources and platforms, and new approaches such as data blending, schema-on-read and data refineries Need to modify data governance approaches o No longer practical to rigidly control and govern all forms of data – use different levels of governance based on security, compliance, quality and retention needs Slide compliments of Colin White – BI Research, Inc.
  • 24. Copyright © Intelligent Solutions, Inc. 2015 All Rights Reserved Agenda  Extending the Data Warehouse Architecture  Use Cases for a Modern BI Environment  Things to Ponder…  XDW – Real World Examples 24
  • 25. Extended Data Warehouse Architecture – Real World Examples
  • 26. Extended Data Warehouse Architecture - Recap Traditional EDW environment Investigative computing platform Analytic tools & applications Other internal & external structured & multi-structured data Real-time streaming data Slide created by Colin White – BI Research, Inc. Operational real-time environment RT analysis engineOperational systems BI services Data refinery Data integration platform
  • 27. Analyzes weather data to provide insurance to farmers who can lock in profits even in the case of drought, excessive rains or other adverse weather conditions • Aggregation of very large dynamic data sets • Mix of cloud, web and internal data • Excel and Tableau to generate reports on risk assessment, and recommend coverage/price policies
  • 30. Deployment in Amazon EC2 Cloud
  • 31. Telematics - IoT Data services access to machine generated data. Business use: predictive maintenance services Major Heavy Equipment Manufacturer
  • 32. Telematics Project Hadoop Cluster OSI PI Dealer Maintenance Parts Inventory Virtual Views Dealer/Customer Dashboards
  • 33. Summary – Things to Remember ■ The traditional enterprise Data Warehouse architecture needs to evolve to embrace Big Data ■ However this doesn’t mean that the enterprise Data Warehouse is not needed ■ Match the technologies and costs to the business needs ■ You will need to combine data from both the traditional and new environments ■ Data Virtualization will be an essential component of the Extended Data Warehouse
  • 34. Q&A
  • 35. Data Virtualization – Next Steps Move forward at your own pace  Download Denodo Express – The fastest way to Data Virtualization  Denodo Community: Documents, Videos, Tutorials, and more. Move forward with one of our Data Virtualization experts  Phone: (+1) 877-556-2531 (NA)  Phone: (+44) (0)20 7869 8053 (EMEA)  Email: info@denodo.com | www.denodo.com