1. DATA VIRTUALIZATION PACKED LUNCH
WEBINAR SERIES
Sessions Covering Key Data Integration Challenges
Solved with Data Virtualization

2. Bridging the Last Mile:
Getting Data to the People Who Need It
Chris Walters
Senior Data Solutions Consultant, Denodo
Paul Moxon
SVP Data Architectures & Chief Evangelist, Denodo

3. Agenda
1.What is the ‘Last Mile’?
2.Logical Architectures to the Rescue
3.Data Virtualization as a Data Access Layer
4.Denodo Platform Demo
5.Summary
6.Q&A
7.Next Steps
3

4. What is the ‘Last Mile’?
Term from Logistics domain
• Getting the product from the Distribution
Center to the customer
• Personalized deliveries rather than bulk
shipping
Also used in Telecoms
• The ‘gap’ from the broadband switch to
the home or office
• Or the wireless gap between the wireless
base station and the user’s mobile device
4

5. Why is the Last Mile Important?
This is delivering the requested product to the
customer
• In our case, delivering data to the user
• Change from bulk movement of data (ETL/ELT) to
delivering data specific to users needs
This used to be ‘Data Marts’ – extracted subsets
of data for a specific use
But, the information ‘landscape’ is getting more
complex, more diverse, and more distributed
• The old ETL to the Data Warehouse and then ETL to
create Data Marts doesn’t cut it anymore…
5

6. Modern Data Architecture
6

7. Why is this a Problem?
7
IT DepartmentBusiness
“You’re too slow, too
expensive, and never
deliver what I want.”
“You can’t make up your
mind, keep adding
features, and never see
the big picture.”
Casual User: “Just
forget it.”
Power User: “Just give
me a data dump.”
BU Leader: “We’ll do it
ourselves.”
“I’d rather be doing
something else than
taking your order.”
“You’ll come crawling
back to us soon.”

8. 8
Gartner – The Evolution of Analytical Environments
This is a Second Major Cycle of Analytical Consolidation
Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
Operational
Application
Operational
Application
Cube
Operational
Application
Cube
? Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
1980s
Pre EDW
1990s
EDW
2010s2000s
Post EDW
Time
LDW
Operational
Application
Operational
Application
Operational
Application
Data
Warehouse
Data
Warehouse
Data
Lake
?
Logical Data Warehouse
Data Warehouse
Data Lake
Marts
ODS
Staging/Ingest
Unified analysis
› Consolidated data
› "Collect the data"
› Single server, multiple nodes
› More analysis than any
one server can provide
©2018 Gartner, Inc.
Unified analysis
› Logically consolidated view of all data
› "Connect and collect"
› Multiple servers, of multiple nodes
› More analysis than any one system can provide
ID: 342254
Fragmented/
nonexistent analysis
› Multiple sources
› Multiple structured sources
Fragmented analysis
› "Collect the data" (Into
› different repositories)
› New data types,
› processing, requirements
› Uncoordinated views

9. 9
Gartner – The Evolution of Analytical Environments
This is a Second Major Cycle of Analytical Consolidation
Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
Operational
Application
Operational
Application
Cube
Operational
Application
Cube
? Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
1980s
Pre EDW
1990s
EDW
2010s2000s
Post EDW
Time
LDW
Operational
Application
Operational
Application
Operational
Application
Data
Warehouse
Data
Warehouse
Data
Lake
?
Unified analysis
› Consolidated data
› "Collect the data"
› Single server, multiple nodes
› More analysis than any
one server can provide
©2018 Gartner, Inc.
Unified analysis
› Logically consolidated view of all data
› "Connect and collect"
› Multiple servers, of multiple nodes
› More analysis than any one system can provide
ID: 342254
Fragmented/
nonexistent analysis
› Multiple sources
› Multiple structured sources
Fragmented analysis
› "Collect the data" (Into
› different repositories)
› New data types,
› processing, requirements
› Uncoordinated views
Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
Logical Data Warehouse
Data Warehouse
Data Lake
Marts
ODS
Staging/Ingest
Data Virtualization
• Limited flexibility
• Data duplication
• Limited support for operational BI
• Lack of integration with big data
technologies
• Non-Trivial support for bi-modal BI
• Too big to move
• Complex transformations moved to users
• Security
• Weak Metadata
√ Improved Time to Market by 50 to 90%
√ Improved Report Consistency
√ Reduce Duplication of Data
√ Improve Transparency
√ Reduced development Cost
√ Future Proof the architecture against
technology changes

10. 10
Gartner – Logical Data Warehouse
“Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry
Cook, Gartner April 2018
DATA VIRTUALIZATION

11. Gartner, Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical
Needs, May 2018
“When designed properly, Data Virtualization can speed data
integration, lower data latency, offer flexibility and reuse, and reduce
data sprawl across dispersed data sources.
Due to its many benefits, Data Virtualization is often the first step for
organizations evolving a traditional, repository-style data warehouse
into a Logical Architecture”

12. 12
Benefits of a Virtual Data Layer
 A Virtual Layer improves decision making and shortens development cycles
• Surfaces all company data from multiple repositories without the need to replicate all
data into a data warehouse or data lake
• Eliminates data silos: allows for on-demand combination of data from multiple sources
 A Virtual Layer broadens usage of data
• Improves governance and metadata management to avoid “data swamps”
• Decouples data source technology. Access normalized via SQL or web services
• Allows controlled access to the data with low grain security controls
 A Virtual Layer offers performant access
• Leverages the processing power of the existing sources controlled by Denodo’s optimizer
• Processing of data for sources with no processing capabilities (e.g. files)
• Caching and ingestion engine to persist data when needed
TTV
USAGE
PERFORMANCE

13. Customer Case Study
13

14. Customer Case Study - FESTO
14
• Founded 1925
• Annual revenues (FY
2018) €3.2 B
• Over 21,000
employees
• Headquarters in
Germany
• World´s leading
supplier of
automation
technology and
technical education.
BUSINESS NEED
• Optimize operational efficiency, automate manufacturing processes,
and deliver on-demand services to business consumers
• Find smarter ways to aggregate and analyze data
• An agile solution that enables the monetization of customer-facing
data products
• Free business users from IT reliance to become self-sufficient with
reporting and analysis
THE CHALLENGE:
Find an agile way to integrate data from existing silos, including data
warehouse, machine data, and others, that will reduce dependencies
from business users on IT and provides quick turnaround and flexibility.

15. Customer Case Study - FESTO
15
SOLUTION:
• Festo developed a Big
Data Analytics
Framework to provide a
data marketplace to
better support the
business
• Using the Denodo
Platform to integrate
data from numerous on-
prem and cloud systems
in real-time
• A unified layer for
consistent data access
and governance across
different data silos

16. 16
FESTO – Digital Transformation

17. Product Demonstration
Bridging the Last Mile: Getting Data to the
People Who Need It
17
Chris Walters
Senior Data Solutions Consultant, Denodo

18. 18
The Architecture
Sources
2. Data
Model
Combine,
Transform
&
Semantics
3.
Publish
1.
Source
Abstracti
on
Consuming
Applications
4.Dev/Ops

19. 19
DATA CONSUMERS
DISPARATE DATA SOURCES
SQL Queries
(JDBC, ODBC, ADO.NET)
Web Services
(SOAP, REST, OData)
Web-based catalog
& search
Secure delivery
(SSL/TLS)
DATA CONSUMERS
MPP Processing
Relational Cache
Corporate
Security
Monitoring &
Auditing
Metadata
Repository
Execution
Engine &
Optimizer
A Modern Data Virtualization Architecture
DATA
VIRTUALIZATION

20. 20
What’s the demo scenario
We have a traditional Data Warehouse in Oracle
To offload the warehouse end expand our data sets with IoT data, we
have acquired a Hadoop cluster
We are big users of SaaS solutions
Need to easily build reports using data coming from these sources

21. 21
Example
What’s the impact of a new
marketing campaign for each
country?
 Historical sales data offloaded
to Hadoop cluster for cheaper
storage
 Marketing campaigns managed
in an external cloud app
 Country is part of the customer
details table, stored in the DW Sources
Combine,
Transfor
m
&
Integrate
Consume
Base View
Source
Abstraction
join
group by
state
join
Sales Campaign Customer

22. Demo
22

23. Key Takeaways
23

24. 1. Information architectures are getting more
complex, more diverse, and more distributed
2. Traditional technologies and data replication don’t
cut it anymore
3. Data virtualization makes it quick and easy to
expose data from multiple source to your users
4. Data virtualization provides a governance and
management infrastructure required for successful
data management
Key Takeaways

26. 26
Next Steps
Access Denodo Platform in the Cloud!
Take a Test Drive today!
www.denodo.com/TestDrive
G E T S TA R T E D TO DAY

27. Thank you!
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