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Ten Pillars of World Class
Data Virtualization
Suresh Chandrasekaran, Senior VP
Agenda1.Data Virtualization Evolution
2.Ten Pillars of “World-Class” DV
3.Enterprise Maturity: A Client’s Journey
4.Impacting CxO Priorities
Data Virtualization Evolution
3
Feature to Enterprise Fast Data Strategy
Data
Integration
Data
Federation
Data
Integration
Data
Virtualization
Data
Federation
Data
Virtualization
Self-Service
Data
Data
Integration
Data
Governance
Single View
Data Services
Database
Federation
“Feature”
“Style”
“Technology”
“Enterprise Fast Data Strategy”
4
Ten Pillars of ‘World Class’ Data Virtualization
What are they?
Robust Security
Strong Integration
Impact Management
Dynamic Performance
Broad Uses & Users
Flexible Data Model
Hybrid Execution
Data Governance
Ease of Use
Broad Data Sources Access anything and everything – Enterprise, Cloud, Big Data, Social Media, Web, Files, PDF …
Support all 5 use cases – Informational, Analytical, Operational, User Self-Service, Data Mgmt
Past and future-proof – Leverage past skills and support yet-to-be-invented sources easily
Powerhouse of integration, transformation, data quality functions; fully extensible
Your choice or automatic – Full real-time to full batch, with varying shades of cache
Dynamic query optimization – Move real-time queries to data using statistics and network info
Prioritize and protect – Manage impact to source systems and service levels
Knowledge to govern – models, entity relations, data lineage, change impact, usage, export
Rapid Learning to Expert Mode – Full lifecycle, intuitive graphical and script tools, tutorials
Integrated and fine-grained – Single sign-on, roles, encryption, masking, view to cell level
1. Broad Data Source Connectivity
Access any and every data source and virtualize with ease
100s of sources: Enterprise,
Cloud, Big Data, Web, Files… +SDK
Graphical wizards, vendor-specific
adapters – 3 clicks to virtualize
Certification & enhanced support
for key enterprise sources
Many paths to big data
Unstructured Web, Index, Content
All connectors are included
Find more details at: datavirtualization.blog
http://www.datavirtualizationblog.com/?s=sources
Connection
Wizard
5
2. Support Broad Use Cases and User Types
Top 5 – Informational, Analytical, Operational, User Self-Service, Data Mgmt
Flexible Publishing data access via 12+
protocols; Support Search, Browse, Query
SQL access via JDBC, ODBC, ADO.NET
Push-down optimization of analytic
queries to more analytic sources
Information Self Service Tool for
discovery and exploration
Data Services – SOAP, REST, Odata - with
advanced features
Metadata & Data Management APIs
6
Find more details at: datavirtualization.blog
http://www.datavirtualizationblog.com/data-virtualization-
main-use-cases/
3. Flexible, Resilient Virtual Data Model
Past and Future-proof – Leverage past skills and support yet-to-be-invented sources
Unique Extended Relational Model –
relational, hierarchical, semantic,
unstructured, etc. data types supported
Easily learned with wide industry support
Support lack of pre-defined schema
Advantages in all stages – importing
data sources, performance, management,
publishing
Represent data source query capabilities
natively
Allow combination of structured and
unstructured queries in the same query
Find more details at: datavirtualization.blog
http://www.datavirtualizationblog.com/thinking-outside-the-
graph-data-virtualization-and-graph-databases/
7
4. Strong Integration Capabilities
Powerhouse of integration, transformation, data quality functions; fully extensible
Library of transformation, quality,
matching functions with auto-correct
Operators for both data and metadata,
structured and unstructured
Graphical, model-driven wizards and
script editor for logical integration
Custom functions provide extensibility –
build your own or invoke external tools for
DQ, transform, business rules, etc.
Adding functions for special data types
e.g. geo-spatial, semantic, fuzzy match
Find more details at: datavirtualization.blog
http://www.datavirtualizationblog.com/extensibility-key-
aspect-data-virtualization-platform/
8
5. Hybrid Execution – Real-time or Right-time
Manual or automatic – Full real-time to full batch, with varying shades of cache
Dynamic real-time integration
with high performance
Integrated intelligent caching –
full, partial, incremental; disk, MPP,
or in-memory; manual or automatic
Integrated Scheduler (batch / ETL)
for data persistence, export, etc.
Mix and Match above for specific
nodes, sources, or query types
Achieve performance + agility
Find more details at: datavirtualization.blog
http://www.datavirtualizationblog.com/intelligent-caching-in-
data-virtualization/
9
Cached Views
Real-time Views
Hybrid RT / Cached View
RT View or Batch Export (ETL) of Results
6. Dynamic Query Optimization
Best Performance Even When Processing Billions of Rows
Move processing to the data
paradigm
Fully automated optimization
decisions
Considers characteristics of
disparate sources
Considers statistics and cost-
based optimization
Find more details at: datavirtualization.blog
http://www.datavirtualizationblog.com/myths-in-data-
virtualization-performance/
10
7. Robust Security - Integrated, Fine-grained
Support single sign-on, groups & roles, encryption, masking, granular access control
Virtual data and source pass-
through security
Integrated with LDAP, AD, SSO
Fine-grained access to view,
row, column, cell, masking
Policy-based custom security
Find more details at: denodo.com
http://www.denodo.com/en/document/case-study/how-
pantex-used-data-virtualization-share-sensitive-information-
across
11
8. Resource Management
Prioritize and protect – Manage impact to source systems and service levels
Integrated resource manager for workload
management and source protection
Allocate resources to users/applications
according to business priorities
Set rules for resources, actions, priority,
query throttling, user priorities, etc.
Custom policies support for flexibility
Find more details at: datavirtualization.blog
http://www.datavirtualizationblog.com/quality-of-service-in-
your-business-apps/
12
9. Data Governance
Knowledge of models, entity relations, data lineage, change impact, usage, export
Support both top-down & bottom-
up modelling, export/import
Extensive metadata, data lineage,
change impact info
Metadata API integrates with data
modelling, BI, dictionary tools
Data services catalog with usage,
audit/log, security information
Find more details at: denodo.com
http://www.denodo.com/en/media-coverage/leveraging-
virtualization-streamline-data-management
13
10. Ease of Use Across Full Data Lifecycle
Rapid Learning to Expert Mode - intuitive graphical and script tools, management
Ease of Use – Updated developer
GUI, tools, tutorials, tips
Business user self-service BI,
discovery and exploration interface
Fully integrated with version
control; Migration wizards
Extensive in-tool monitoring and
management and external APIs
Find more details at: datavirtualization.blog
http://www.datavirtualizationblog.com/data-exploration-and-
self-service-bi-welcome-to-the-dataweb/
14
15
Ten Pillars of ‘World Class’ Data Virtualization
Reaching Enterprise Maturity Takes 16-years of Focus
Robust Security
Strong Integration
Impact Management
Dynamic Performance
Broad Uses & Users
Flexible Data Model
Hybrid Execution
Data Governance
Ease of Use
Broad Data Sources Access anything and everything – Enterprise, Cloud, Big Data, Social Media, Web, Files, PDF …
Support all 5 use cases – Informational, Analytical, Operational, User Self-Service, Data Mgmt
Past and future-proof – Leverage past skills and support yet-to-be-invented sources easily
Powerhouse of integration, transformation, data quality functions; fully extensible
Your choice or automatic – Full real-time to full batch, with varying shades of cache
Dynamic query optimization – Move real-time queries to data using statistics and network info
Prioritize and protect – Manage impact to source systems and service levels
Knowledge to govern – models, entity relations, data lineage, change impact, usage, export
Rapid Learning to Expert Mode – Full lifecycle, intuitive graphical and script tools, tutorials
Integrated and fine-grained – Single sign-on, roles, encryption, masking, view to cell level
Product + Services + TCO/ROI = Success Delivered!
16
11. Solutions and Services
• Solution Expertise & Frameworks
• Training & Best Practices
• Denodo and Partner Network
• Build DV CoE
12. Flexible Deployment & Pricing
• Denodo Express – Get Started Free
• Denodo in Amazon Cloud – Infinitely
Elastic
• Denodo Server-based Licenses –
Perpetual or Subscription
• Denodo Enterprise Unlimited – All you
can eat
1st
Internal
Education, ROI,
Communication,
Governance
Data
Strategy &
ROI
Architectur
e & Use
Cases
Developme
nt &
Operations
Best
Practices,
Build CoE
CxOs, Enterprise
Architects, LOB
Execs
POC, Pilot, Development,
Integration, Testing,
Performance
Agile BI, Big Data
Analytics, Logical
DWH, Customer 360,
Cloud, Data Services,
etc..
1st
Intel: POC in 2013
17
- “Game Changer” in 2015
Enterprise Maturity: A client’s journey
Intel: DV Benefits, Detail Metrics
18
Value Driver Metric Goal Actual
Time to Develop Time to develop web service in days 50% 90%
Time to Deploy Time to Deploy web service in days 50% 90%
TTM Overall time it takes to make web
service available for use
60% 90%
Time to Engage Time it takes for business to engage
with IT
75% 75%
Performance Performance of web services 50% 60%
Impact Analysis How fast can we perform impact
analysis
50% 90%
Enterprise Architectural
Alignment
Ease at which data from disparate
sources can be integrated
Security, data
classification
High
Enterprise Maturity: A client’s journey
Enterprise DV @ Confluence of Ecosystems
19
Key Enabler to Large Projects in an Increasingly Data-Driven World
Agile BI and
Self-Service BI
Big Data and
Advanced Analytics
App Development
and Data Services
- For Digital, Cloud,
Mobility
Data
Virtualization
Denodo Adds Value to Entire CIO Agenda
20
Enabling Business Agility
Focus:
Making LOB partners agile i.e. launch new
products, get closer to customer, offer data
visibility and rapid data provisioning
The Enterprise Data Marketplace. Enabling
Self-Service
Efficiency in Data
Operations
Focus:
Reduce costs and complexity, minimize data
replication, foster data reusability and
collaboration
Driving operational efficiencies and
reduced cost
Taming the data “mess”
Focus:
Data Governance, Discovery, Unified
Data Modeling, Security, Data Auditing
Unifying a diverse universe of data
assets and helping to enforce
enterprise data policies
Data-Centricity in
Business
Focus:
Integration layer for Big Data & other corporate data
assets. IoT innovative projects. Data-driven business
models. Data science and analytics.
Innovating through big data, adding new
sources for enterprise use, Advanced Analytics
21
Is Denodo 6.0 Enterprise Ready? You Bet!
250+ customers across 30+ industries
HEADQUARTERS
Palo Alto, CA.
DENODO OFFICES, CUSTOMERS, PARTNERS
Global presence throughout North America,
EMEA, APAC, and Latin America.
LEADERSHIP
 Longest continuous focus on data
virtualization and data services.
 Product leadership.
 Solutions expertise.
CUSTOMERS
250+ customers, including many
F500 and G2000 companies across every
major industry have gained significant
business agility and ROI.
22
Is Denodo 6.0 Enterprise Ready? You Bet!
Public Sector
Financial Services
Telecommunications
Healthcare
Technology
Manufacturing
Insurance
Retail
Pharma / Biotech
Energy
Thanks!
www.denodo.com info@denodo.com

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Ten Pillars of World Class Data Virtualization

  • 1. Ten Pillars of World Class Data Virtualization Suresh Chandrasekaran, Senior VP
  • 2. Agenda1.Data Virtualization Evolution 2.Ten Pillars of “World-Class” DV 3.Enterprise Maturity: A Client’s Journey 4.Impacting CxO Priorities
  • 3. Data Virtualization Evolution 3 Feature to Enterprise Fast Data Strategy Data Integration Data Federation Data Integration Data Virtualization Data Federation Data Virtualization Self-Service Data Data Integration Data Governance Single View Data Services Database Federation “Feature” “Style” “Technology” “Enterprise Fast Data Strategy”
  • 4. 4 Ten Pillars of ‘World Class’ Data Virtualization What are they? Robust Security Strong Integration Impact Management Dynamic Performance Broad Uses & Users Flexible Data Model Hybrid Execution Data Governance Ease of Use Broad Data Sources Access anything and everything – Enterprise, Cloud, Big Data, Social Media, Web, Files, PDF … Support all 5 use cases – Informational, Analytical, Operational, User Self-Service, Data Mgmt Past and future-proof – Leverage past skills and support yet-to-be-invented sources easily Powerhouse of integration, transformation, data quality functions; fully extensible Your choice or automatic – Full real-time to full batch, with varying shades of cache Dynamic query optimization – Move real-time queries to data using statistics and network info Prioritize and protect – Manage impact to source systems and service levels Knowledge to govern – models, entity relations, data lineage, change impact, usage, export Rapid Learning to Expert Mode – Full lifecycle, intuitive graphical and script tools, tutorials Integrated and fine-grained – Single sign-on, roles, encryption, masking, view to cell level
  • 5. 1. Broad Data Source Connectivity Access any and every data source and virtualize with ease 100s of sources: Enterprise, Cloud, Big Data, Web, Files… +SDK Graphical wizards, vendor-specific adapters – 3 clicks to virtualize Certification & enhanced support for key enterprise sources Many paths to big data Unstructured Web, Index, Content All connectors are included Find more details at: datavirtualization.blog http://www.datavirtualizationblog.com/?s=sources Connection Wizard 5
  • 6. 2. Support Broad Use Cases and User Types Top 5 – Informational, Analytical, Operational, User Self-Service, Data Mgmt Flexible Publishing data access via 12+ protocols; Support Search, Browse, Query SQL access via JDBC, ODBC, ADO.NET Push-down optimization of analytic queries to more analytic sources Information Self Service Tool for discovery and exploration Data Services – SOAP, REST, Odata - with advanced features Metadata & Data Management APIs 6 Find more details at: datavirtualization.blog http://www.datavirtualizationblog.com/data-virtualization- main-use-cases/
  • 7. 3. Flexible, Resilient Virtual Data Model Past and Future-proof – Leverage past skills and support yet-to-be-invented sources Unique Extended Relational Model – relational, hierarchical, semantic, unstructured, etc. data types supported Easily learned with wide industry support Support lack of pre-defined schema Advantages in all stages – importing data sources, performance, management, publishing Represent data source query capabilities natively Allow combination of structured and unstructured queries in the same query Find more details at: datavirtualization.blog http://www.datavirtualizationblog.com/thinking-outside-the- graph-data-virtualization-and-graph-databases/ 7
  • 8. 4. Strong Integration Capabilities Powerhouse of integration, transformation, data quality functions; fully extensible Library of transformation, quality, matching functions with auto-correct Operators for both data and metadata, structured and unstructured Graphical, model-driven wizards and script editor for logical integration Custom functions provide extensibility – build your own or invoke external tools for DQ, transform, business rules, etc. Adding functions for special data types e.g. geo-spatial, semantic, fuzzy match Find more details at: datavirtualization.blog http://www.datavirtualizationblog.com/extensibility-key- aspect-data-virtualization-platform/ 8
  • 9. 5. Hybrid Execution – Real-time or Right-time Manual or automatic – Full real-time to full batch, with varying shades of cache Dynamic real-time integration with high performance Integrated intelligent caching – full, partial, incremental; disk, MPP, or in-memory; manual or automatic Integrated Scheduler (batch / ETL) for data persistence, export, etc. Mix and Match above for specific nodes, sources, or query types Achieve performance + agility Find more details at: datavirtualization.blog http://www.datavirtualizationblog.com/intelligent-caching-in- data-virtualization/ 9 Cached Views Real-time Views Hybrid RT / Cached View RT View or Batch Export (ETL) of Results
  • 10. 6. Dynamic Query Optimization Best Performance Even When Processing Billions of Rows Move processing to the data paradigm Fully automated optimization decisions Considers characteristics of disparate sources Considers statistics and cost- based optimization Find more details at: datavirtualization.blog http://www.datavirtualizationblog.com/myths-in-data- virtualization-performance/ 10
  • 11. 7. Robust Security - Integrated, Fine-grained Support single sign-on, groups & roles, encryption, masking, granular access control Virtual data and source pass- through security Integrated with LDAP, AD, SSO Fine-grained access to view, row, column, cell, masking Policy-based custom security Find more details at: denodo.com http://www.denodo.com/en/document/case-study/how- pantex-used-data-virtualization-share-sensitive-information- across 11
  • 12. 8. Resource Management Prioritize and protect – Manage impact to source systems and service levels Integrated resource manager for workload management and source protection Allocate resources to users/applications according to business priorities Set rules for resources, actions, priority, query throttling, user priorities, etc. Custom policies support for flexibility Find more details at: datavirtualization.blog http://www.datavirtualizationblog.com/quality-of-service-in- your-business-apps/ 12
  • 13. 9. Data Governance Knowledge of models, entity relations, data lineage, change impact, usage, export Support both top-down & bottom- up modelling, export/import Extensive metadata, data lineage, change impact info Metadata API integrates with data modelling, BI, dictionary tools Data services catalog with usage, audit/log, security information Find more details at: denodo.com http://www.denodo.com/en/media-coverage/leveraging- virtualization-streamline-data-management 13
  • 14. 10. Ease of Use Across Full Data Lifecycle Rapid Learning to Expert Mode - intuitive graphical and script tools, management Ease of Use – Updated developer GUI, tools, tutorials, tips Business user self-service BI, discovery and exploration interface Fully integrated with version control; Migration wizards Extensive in-tool monitoring and management and external APIs Find more details at: datavirtualization.blog http://www.datavirtualizationblog.com/data-exploration-and- self-service-bi-welcome-to-the-dataweb/ 14
  • 15. 15 Ten Pillars of ‘World Class’ Data Virtualization Reaching Enterprise Maturity Takes 16-years of Focus Robust Security Strong Integration Impact Management Dynamic Performance Broad Uses & Users Flexible Data Model Hybrid Execution Data Governance Ease of Use Broad Data Sources Access anything and everything – Enterprise, Cloud, Big Data, Social Media, Web, Files, PDF … Support all 5 use cases – Informational, Analytical, Operational, User Self-Service, Data Mgmt Past and future-proof – Leverage past skills and support yet-to-be-invented sources easily Powerhouse of integration, transformation, data quality functions; fully extensible Your choice or automatic – Full real-time to full batch, with varying shades of cache Dynamic query optimization – Move real-time queries to data using statistics and network info Prioritize and protect – Manage impact to source systems and service levels Knowledge to govern – models, entity relations, data lineage, change impact, usage, export Rapid Learning to Expert Mode – Full lifecycle, intuitive graphical and script tools, tutorials Integrated and fine-grained – Single sign-on, roles, encryption, masking, view to cell level
  • 16. Product + Services + TCO/ROI = Success Delivered! 16 11. Solutions and Services • Solution Expertise & Frameworks • Training & Best Practices • Denodo and Partner Network • Build DV CoE 12. Flexible Deployment & Pricing • Denodo Express – Get Started Free • Denodo in Amazon Cloud – Infinitely Elastic • Denodo Server-based Licenses – Perpetual or Subscription • Denodo Enterprise Unlimited – All you can eat 1st Internal Education, ROI, Communication, Governance Data Strategy & ROI Architectur e & Use Cases Developme nt & Operations Best Practices, Build CoE CxOs, Enterprise Architects, LOB Execs POC, Pilot, Development, Integration, Testing, Performance Agile BI, Big Data Analytics, Logical DWH, Customer 360, Cloud, Data Services, etc.. 1st
  • 17. Intel: POC in 2013 17 - “Game Changer” in 2015 Enterprise Maturity: A client’s journey
  • 18. Intel: DV Benefits, Detail Metrics 18 Value Driver Metric Goal Actual Time to Develop Time to develop web service in days 50% 90% Time to Deploy Time to Deploy web service in days 50% 90% TTM Overall time it takes to make web service available for use 60% 90% Time to Engage Time it takes for business to engage with IT 75% 75% Performance Performance of web services 50% 60% Impact Analysis How fast can we perform impact analysis 50% 90% Enterprise Architectural Alignment Ease at which data from disparate sources can be integrated Security, data classification High Enterprise Maturity: A client’s journey
  • 19. Enterprise DV @ Confluence of Ecosystems 19 Key Enabler to Large Projects in an Increasingly Data-Driven World Agile BI and Self-Service BI Big Data and Advanced Analytics App Development and Data Services - For Digital, Cloud, Mobility Data Virtualization
  • 20. Denodo Adds Value to Entire CIO Agenda 20 Enabling Business Agility Focus: Making LOB partners agile i.e. launch new products, get closer to customer, offer data visibility and rapid data provisioning The Enterprise Data Marketplace. Enabling Self-Service Efficiency in Data Operations Focus: Reduce costs and complexity, minimize data replication, foster data reusability and collaboration Driving operational efficiencies and reduced cost Taming the data “mess” Focus: Data Governance, Discovery, Unified Data Modeling, Security, Data Auditing Unifying a diverse universe of data assets and helping to enforce enterprise data policies Data-Centricity in Business Focus: Integration layer for Big Data & other corporate data assets. IoT innovative projects. Data-driven business models. Data science and analytics. Innovating through big data, adding new sources for enterprise use, Advanced Analytics
  • 21. 21 Is Denodo 6.0 Enterprise Ready? You Bet! 250+ customers across 30+ industries HEADQUARTERS Palo Alto, CA. DENODO OFFICES, CUSTOMERS, PARTNERS Global presence throughout North America, EMEA, APAC, and Latin America. LEADERSHIP  Longest continuous focus on data virtualization and data services.  Product leadership.  Solutions expertise. CUSTOMERS 250+ customers, including many F500 and G2000 companies across every major industry have gained significant business agility and ROI.
  • 22. 22 Is Denodo 6.0 Enterprise Ready? You Bet! Public Sector Financial Services Telecommunications Healthcare Technology Manufacturing Insurance Retail Pharma / Biotech Energy