Watch full webinar here: https://bit.ly/32TT2Uu Data virtualization is not just for self-service, it’s also a first-class citizen when it comes to modern data platform architectures. Technology has forced many businesses to rethink their delivery models. Startups emerged, leveraging the internet and mobile technology to better meet customer needs (like Amazon and Lyft), disrupting entire categories of business, and grew to dominate their categories. Schedule a complimentary Data Virtualization Discovery Session with g2o. Traditional companies are still struggling to meet rising customer expectations. During this webinar with the experts from g2o and Denodo we covered the following: - How modern data platforms enable businesses to address these new customer expectation - How you can drive value from your investment in a data platform now - How you can use data virtualization to enable multi-cloud strategies Leveraging the strategy insights of g2o and the power of the Denodo platform, companies do not need to undergo the costly removal and replacement of legacy systems to modernize their systems. g2o and Denodo can provide a strategy to create a modern data architecture within a company’s existing infrastructure.

1. Self Service Analytics
November 2020

2. © 2019 g2o LLC; proprietary and confidential
Uttam Channegowda
Practice Director
Big Data and Data Engineering
g2o
Speakers
Click icon to
add picture
Paul Moxon
SVP Data Architectures
Denodo Technologies

3. about g2o
3
400+
analysts, designers,
developers,
engineers,
researchers, and
strategists
Largest digital
experience and
technology shop
based in Ohio technology,
data, experience
design
We stay close to
our clients
for speed, efficiency,
and deep
collaboration
25+ YEARS
EXPERIENCE
© 2020 g2o, LLC; proprietary and confidential

4. strategically organizing and architecting your data
4© 2020 g2o, LLC; proprietary and confidential
Data s trategy
Accelerated by best-practice templates, automation, and a deep partner
network, our experts help you prioritize data efforts to capture
opportunities and support your business goals.
Modern data
platforms
We assess, reimagine, and re-platform your data environments, so you can
economically and sustainably leverage advanced capabilities to turn data
from a cost center to a growth engine.
Mas ter data management
We help you ensure the integrity of your essential data assets – especially
your customer and product data, to support guided selling, dynamic
pricing, and personalization.
S ingle view of the
cus tomer
We help you achieve a singular and complete representation of your
customer data, and analyze customer behavior, so you can better target
and personalize customer interactions.

5. 1 2 3 4 5
modern
data
platform
analytics and
actionable
insights
seamless
customer
experience
automation and
personalization
tools
optimization with
on-going
measures and
KPIs
5 components of a data-driven organization
5

6. 4 common gaps
• customer data lacks quality that is needed for analysis or
personalization
• customer data is not organized or accessible to support analytics or
drive experiences
• customer data is siloed across multiple systems and the customer
view is incomplete
• customer data is not integrated into other systems that can
personalize the customer experience
where organizations struggle
6

7. self-service users are always waiting on their data
© 2019 g2o LLC; proprietary and confidential
data warehouses MDM platforms data lakes
Plagued by gaps in
data governance
limited to a small subset of core data,
not easily accessible to business
analysts
the up-front effort of developing a
schema pushed to the data consumption
team

8. © 2019 g2o LLC; proprietary and confidential
80% Data Preparation

9. drivers of self-service analytics
data democratization an integral part of being a data-driven organization
disruptive events COVID-19, Japan earthquake, Asian tsunami
data-driven innovation according to Forrester, between 60% and 73% of all data within an
enterprise goes unused for analytics
© 2019 g2o LLC; proprietary and confidential

10. 1. Not all data needs to be
integrated
2. Data quality is in the eye of the
beholder
3. Combining datasets does not
always need to be an IT project
thinking differently about
data
© 2019 g2o LLC; proprietary and confidential

11. © 2019 g2o LLC; proprietary and confidential
Sometimes, being
directionally
correct is good
enough

12. Find the right balance where IT’s charter to govern and
secure data can peacefully coexist with the business’
need for speed to market
The reality is that shadow IT will continue to exist and
truly does serve a purpose for specific analytics use
cases
controlled chaos
© 2019 g2o LLC; proprietary and confidential

13. 1. Data abstraction
2. Zero replication, zero relocation
3. Real-time information
4. Self-service data services
5. Centralized metadata, security &
governance
6. Location-agnostic architecture for
multi-cloud, hybrid acceleration
data virtualization as a self-
service architecture
© 2019 g2o LLC; proprietary and confidential

14. © 2019 g2o LLC; proprietary and confidential
data virtualization is not just for self-service, it’s
also a first-class citizen when it comes to modern
data platform architectures

15. 15
Gartner – The Evolution of Data Architectures
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
?
LDW
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

16. 16
Gartner – Logical Data Architecture
“Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry Cook, Gartner April 2018
DATA VIRTUALIZATION

17. 17
Data Virtualization – A Data Fabric Layer
Consume
in business applications
Combine
related data into views
Connect
to disparate data sources
2
3
1
DATA CONSUMERS
DISPARATE DATA SOURCES
Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users
Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word...
Analytical Operational
Less StructuredMore Structured
CONNECT COMBINE PUBLISH
Multiple Protocols,
Formats
Query, Search,
Browse
Request/Reply,
Event Driven
Secure
Delivery
SQL,
MDX
Web
Services
Big Data
APIs
Web Automation
and Indexing
CONNECT COMBINE CONSUME
Share, Deliver,
Publish, Govern,
Collaborate
Discover, Transform,
Prepare, Improve
Quality, Integrate
Normalized views of
disparate data
“Data virtualization
integrates disparate
data sources in real
time or near-real
time to meet
demands for
analytics and
transactional data.”
– Create a Road Map For A
Real-time, Agile, Self-
Service Data Platform,
Forrester Research, Dec 16,
2015

18. 18
How Does It Work?
Development
Lifecycle Mgmt
Monitoring &
Audit
Governance
Security
Development
Tools and SDK
Scheduled Tasks
Data Caching
Query Optimizer
JDBC/ODBC/ADO.Net SOAP / REST WS
U
Customer 360
View
Virtual
Data Mart
View
J
Application
Layer
Business
Layer
Unified
View
Unified
View
Unified
View
Unified
View
A
J
J
Derived
View
Derived
View
J
JS
Transformation
& Cleansing
Data
Source
Layer
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Abstraction

19. 19
Data Virtualization Connects the Users to the Data That They Need
1. Data Virtualization allows you to connect to (almost) any data source
2. You can combine and transform that data into the format needed by the consumer
3. The data can be exposed to the consumers in a format and interface that is usable
by them
• Typically consumers use the tools that they already use – they don’t have to learn new tools
and skills to access the data
4. All of this can be done without copying or moving the data
• The data stays in the original sources (databases, applications, files, etc.) and is retrieved, in
real-time, on demand
Cliffs Notes version (TL;DR)

20. 20
Data Source Connectivity
Relational Databases
• MS SQL*Server (JDBC, ODBC): 2000, 2005, 2008, 2008R2, 2012, 2014,
2016, 2017
• Oracle (JDBC): 8i, 9i, 10g, 11g, 12c, 18c, 19c
• Oracle E-Business Suite (JDBC): 12
• IBM DB2 (JDBC): 8, 9, 10, 11, 12 for LUW; 9,10 for z/OS, AS400
• Informix (JDBC): 7, 12
• Sybase Adaptive Server Enterprise (JDBC): 12, 15
• MySQL (JDBC): 4, 5
• PostgreSQL (JDBC): 8, 9, 10, 11
• Denodo Platform (JDBC): 5.5, 6.0, 7.0, 8.0
- For multi-location architecture deployments
• MS Access (ODBC)
• Apache Derby (JDBC): 10
• Generic (JDBC)
In-Memory Databases
• SAP HANA (JDBC): 1
• Oracle TimesTen (JDBC): 11g
• Oracle 12c In-Memory
• Redis In-memory Cache
Parallel databases and appliances
• GreenPlum (JDBC): 4.2
• HP Vertica (JDBC): 7, 8
• Oracle Exadata (JDBC): X5-2
• ParAccel 8.0.2 (using ParAccel 2.5.0.0 JDBC3g/SSL driver)
• Netezza (JDBC): 4.6, 5.0, 6.0, 7.0
• SybaseIQ (JDBC) 12.x, 15.x
• Teradata (JDBC): 12, 13, 14, 15
• Yellowbrick
Multi-Dimensional Sources
• SAP BW (BAPI/XMLA): 3.x
• SAP BI 7.x (BAPI): 7.x
• Mondrian (XMLA): 3.x
• IBM Cognos TM1
• MS SQL Server Analysis Services 200x
• Essbase (XMLA): 9, 11
Cloud Databases and Data Warehouses
• Amazon Redshift (JDBC)
• Amazon Athena (JDBC)
• Amazon Aurora (JDBC)
• Amazon DynamoDB
• Amazon RDS (JDBC)
• Azure Cosmos DB
• Azure SQL Database
• Azure Synapse Analytics (fka SQL Data Warehouse)
• Databricks Delta Lake
• Google Cloud SQL
• Google BigQuery (JDBC)
• MongoDB Atlas
• Snowflake (JDBC)
Data Lake Storage
• Amazon S3
• Azure Data Lake Storage
• Azure Data Lake Storage Gen 2
• Azure Blob Storage
• Google Cloud Storage
• Parquet (Distributed File System Connector)
• Avro
Big Data
• Apache Hive (JDBC): 0.12, 1.1.0, 1.1.0 for Cloudera 1.2.1
and for Hortonworks 2.0.0
• MapR-XD, MapR-DB, MapR-ES, Hive, and Drill for MapR 6.1
• Amazon Elastic Map-Reduce (EMR)
• Apache HBase (using DenodoConnect connector)
• Impala (JDBC): 2.3
• Google BigTable
• Spark SQL (JDBC): 1.5, 1.6
• Presto (JDBC)
• Databricks 2.x
NoSQL
• MongoDB
• Cassandra
Web Services
• SOAP
• REST (XML, RSS, ATOM, JSON)
• OData v2 and v4
Packaged Applications
• SAP ERP/ECC (BAPIs and RFC tables)
• Oracle E-Business Suite 12
• Siebel
• SAS (SAS JDBC Driver): 7 and higher
Flat and Binary Files
• CSV, pipe-delimited, Regular expression-parsed
• MS Excel xls 97-2003
• MS Excel xlsx 2007 or later
• MS Access
• XML
• JSON
• SAS Files (SAS7BDAT)
All files can be locally accessible or in remote filesystems,
through FTP/ SFTP/FTPS, and in clear, zipped and/or
encrypted format.
Active Directory as source or leveraging security
• LDAP v3
• Microsoft Active Directory 2003, 2008
Cloud, SaaS, Web Sources with Simplified OAuth Security
• Amazon
• Google
• Google Sheets
• Facebook
• LinkedIn
• MS SharePoint (by using the OData connector)
• MS Dynamics 365 Business Central/Customer
Engagement
• Marketo
• ServiceNow
• Salesforce (SOQL)
• NetSuite
• Twitter via APIs with simplified OAuth integration (1.0,
1.0a and 2.0)
• Workday
Indexes and unstructured content
• CMS, file systems, pdf, word, text, email servers,
knowledge bases, indexes
• Elastic Search 6.4, 6.7
Streaming/Messaging Systems
• MQSeries
• SonicMQ
• ActiveMQ
• TIBCO EMS
• Kafka Messaging
• Spark Streams
• IBM Streams
Semantic Repositories
• Semantic repositories in Triple Stores/RDF accessed
through SPARQL endpoints.
• Neo4j Graph Database
Denodo SDK for Custom Connectors
• CouchDB
• Lotus Domino
Web Automation
• Denodo’s ITPilot automates extraction from web
pages
Mainframe
• IMS
• IBM IMS native drivers: 8, 9
• IMS Universal Drivers: 11
Hierarchical databases
• Adabas (SOA Gateway and Denodo’s SOAP
connector): 5, 6
Legacy
• Microsoft FoxPro (ODBC)
The following data sources have been successfully tested
with Denodo using JDBC and ODBC drivers, WS/SOAP
and WS/REST, and DenodoConnect adapters (not
exhaustive list):
• Apache Solr
• IBM BigInsights
• Pivotal HAWQ

21. 21
Protocols and Formats
• SQL Based access via JDBC, ODBC and ADO.NET
• Web Services
• SOAP (XML/JSON)
• REST (JSON/XML)
• OData 2 & 4
• GraphQL
• Open API (a.k.a Swagger)
• Web Parts (for SharePoint), Portlets
• Kafka and JMS listeners for message queues
• Denodo Scheduler for batch process and ‘ETL lite’
Security Options
• Authentication using LDAP or Active Directory
• Kerberos for Single Sign-On (SSO)
• OAuth, OAuth 2.0 (JWT)
• SAML
• SSL/TLS
• WS-Security, X.509 certificates
• Two-Factor Authentication – via identity providers Okta, Duo, etc.
BI/Reporting tools
• Microstrategy, Cognos, Business Objects, Oracle OBIEE
• Tableau, Qlikview, Spotfire, Microsoft PowerBI
• Excel
Analytical Tools/Languages
• SAS, Statistica, SPSS, MatLab
• R, Python, Java, Scala, etc.
• Azure ML Studio, Apache Zeppelin and Jupyter analytics
notebooks
Portals
• SharePoint, Enterprise portals, Web/mobile apps
Enterprise Service Bus
• Oracle Service Bus, Azure Service Bus, TIBCO Active Matrix
Bus
ETL tools
• SAP Data Services, Informatica Powercenter, IBM Data Stage,
Talend ETL
API Management tools
• CA (Layer 7), TIBCO Mashery, Apigee
Publishing Options

22. 22
Decoupling Business and IT
IT: Flexible Source Architecture
Business: Flexible
Tool Choice
IT can now
move at
slower speed
without
affecting the
business
Business can now
make faster and
more
sophisticated
decisions as all
data accessible
by any tool of
choice

23. 23
Multi-cloud future is a reality:
• Risk mitigation
• Mix and match of best of breed tools and
technologies
• Multi-cloud architectures include a mix of
on-premise databases as well
• Organizations won’t be moving to the
cloud overnight and need a layer that
eases the transition
Data Virtualization Enables Cloud Modernization

24. 24
• Data Virtualization has reached the
‘Plateau of Productivity’
• Alternatives are still not mature
enough for mainstream
• Data Lakes still rely on ETL and security
remains a challenge
• ‘No code’ data tools for self-service
(e.g. data Prep tools) have governance
and security issues also.
Data Virtualization is Mainstream…

25. 25
Gartner and Forrester Research Evaluations
Why Denodo?
Forrester Wave: Enterprise Data Virtualization, Q4 2017Forrester Wave: Enterprise Data Fabric, Q2 20202020 Gartner Magic Quadrant for Data Integration Tools

26. 26
Publication Date – 25th August 2020
Gartner Critical Capabilities for Data Integration Tools
Denodo is the only
product with 5.0 score in
Data Virtualization
category

27. Q&A

28. What’s your Next? Request a Discovery Session
Learn how to put
Data Virtualization to work
in your organization!
pages.denodo.com/g2orequest.html
REGISTER NOW

29. Thank you for joining us!
© 2020 g2o LLC; proprietary and confidential