Watch full webinar here: https://bit.ly/2YdstdU Digital Transformation has changed IT the way information services are delivered. The pace of business engagement, the rise of Digital IT (formerly known as “Shadow IT), has also increased demands on IT, especially in the area of Data Management. Data Services exploits widely adopted interoperability standards, providing a strong framework for information exchange but also has enabled growth of robust systems of engagement that can now exploit information that was normally locked away in some internal silo with Data Virtualization. We will discuss how a business can easily support and manage a Data Service platform, providing a more flexible approach for information sharing supporting an ever-diverse community of consumers. Watch this on-demand webinar as we cover: - Why Data Services are a critical part of a modern data ecosystem - How IT teams can manage Data Services and the increasing demand by businesses - How Digital IT can benefit from Data Services and how this can support the need for rapid prototyping allowing businesses to experiment with data and fail fast where necessary - How a good Data Virtualization platform can encourage a culture of Data amongst business consumers (internally and externally)

1. DENODO LUNCH & LEARN
28 September
Data Services and the
Modern Data Ecosystem

2. Presenters for this Session
Chris Day
Director, APAC Sales Engineering, Denodo
Regional Vice President, Sales, ASEAN & Korea, Denodo
Elaine Chan

3. Agenda
1. Data Services and the Modern Data Ecosystem
• Data Ecosystems
• APIs
• Patterns & Practices
• Use Cases
• Capabilities
2. Product Demonstration
3. Q&A
4. Next Steps

4. Data Services and the Modern Data Ecosystem
Regional Vice President, Sales, ASEAN & Korea, Denodo
Elaine Chan

5. Data Eco-Systems

7. 7
The Web (Data Ecosystem)
Instant access to everything
without needing to store anything

8. 8
The Traditional Data Ecosystem
Delayed access to somethings
while needing to store everything

9. 9
The Modern Data Ecosystem
Deployment On-premise Cloud
Processing Batch Real-time
Access Point-to-point Decoupled
Data Models Rigid Flexible
Orchestration Manual Automated

10. 10
Digital Transformation
§ Digital transformation is a strategic initiative for most
organizations.
§ The concept reflects technology’s role in strategic decision-
making, with its ability to automate and simplify business
processes, improve customer relationships, enhance
productivity, and cost savings.
§ Driven from CEO’s office: Highest level of visibility & fully
funded.
§ Gartner – 28% of CIO budget in 2018
§ IDC – 2/3 of CEOs in global 2000 have digital transformation in
the center of their corporate strategy
§ Seen as do-or-die initiative
§ “If you don’t, someone else will”

11. 11
The Rise of Digital IT
And the challenges for traditional IT
§ Often called shadow IT, LOB’s have more sophisticated tools and needs.
§ Digitization of society provides more channels or systems of engagements managed/owned or
interacted with by LOB’s.
§ Operational systems are automated and access to information is vital to help manage operations
(supply chain, order fulfillment, etc.)
§ LOB’s need agile access to information to analyze data, experiment and fail fast where necessary.
§ Increased demand for different types of data including image, video, audio, etc.

12. 12
The Relevance of APIs
§ The internet has created an interconnected
world.
§ Similarly, different processes and applications
within a company also need to communicate
with each other.
§ Web services are the building blocks of this
interconnected world.
§ The concept of “an application exposing
functionality” has evolved into “the web service
is the application” (Microservices)
Definitions
API - an interface
Web Service - a remote API via the web
Data Service - a web service for data
Microservice - an architectural style

13. 13
Modern Data Ecosystem from an App Perspective
https://www.omg.org/cloud/deliverables/CSCC-Cloud-Customer-Architecture-for-Hybrid-Integration.pdf

14. 14
Why Are Data Services Important?
Abstraction
§ Consumer need not concern themselves with the complexities of data acquisition and composition.
§ IT flexibility with limited impact on business
§ Aggregation of data providers
Utilizable
§ Multiple consumers can share the same service for a myriad of use cases (generic, interoperable, flexible consumption patterns)
Governance
§ Data services also perform a critical governance function - They help centralize metrics, monitoring, version management, reuse
of data types, and enforce data visibility and access rules.
Semantics
§ Alignment with logical data models
Controlled Access
§ Single point of interaction

15. 15
Common Scenarios for Data Services

16. Patterns and Practices
Data Virtualization in the API

17. 17
Data Services Layer (Data API)
A data access layer that abstracts underlying data sources and exposes them as
discrete services to form a ‘data API’.
§ Different users and developers across the enterprise can access data in a secure and managed
fashion and share a common data ‘model’
§ Provides secure and managed access to data across the enterprise
§ Provides consistency of data
§ Hides complexity, format, and location of actual data sources
§ Supports many consumption protocols and patterns
Example:
Single data access layer for all development teams to avoid ‘hunting down and interpreting data
differently by project’

18. 18
Data Virtualization for Data-as-a-Service
Denodo provides one-click, zero development REST
web services on top of any data model with full-fledge
capabilities:
§ Support XML, JSON, GeoJSON, RSS and HTML
§ Support for hierarchical structures
§ Authentication with basic HTTP, Kerberos, OAuth 2.0 and SAML
§ Self documented with OpenAPI
§ Available in REST, OData, and GraphQL formats

19. 19
Uses – Service Container

20. 20
Uses – Data Source

21. 21
Uses – API Gateway
Denodo – API Gateway

22. 22
Patterns – Data Access
Pattern Description Use
Aggregation Aggregate result sets for
consumption
Offload processing from front-end to back-end. Exploits pushdown
capability. Also, can be used in conjunction with caching and/or
query acceleration.
Augmentation
(Fusion)
Provide additional calculated
or derived elements
New fields that contain data expanded from existing. Often done to
avoid storing or modifying the underlying system. Examples include
adding geospatial info or formulas sourced from multiple columns.
Blending Link multiple data elements
(from different sources) into
one service
Service that incorporates multiple elements together. May specify
rules for what/when to blend. Data exists in separate repositories
linked by unique identifier(s).
Filtering Limit data returned in result Often driven by security to limit (rows) or restrict (columns) based on
the type of requestor. When used for performance, becomes
Microservice Architecture Pattern.

23. 23
Patterns – Data Access
Pattern Description Use
(Entity) Domains Surface logical data domains
over existing systems
Enable microservices to be grouped together to express particular
(functional) domain areas. Often driven by ownership (centralized or
distributed) and control (e.g. official/curated). Leverage traversable
relationships.
Composite Combine (multiple) other
service calls through a single
“composite” service
Abstract complexity of underlying services, including drivers for
security, transactionality, performance, and modeling.
Sharing Expose service to a different
tier
Typically used to expose “data” to “application” tier or from
“internal” an org to “external.” Often used in association with an API
Management tool and use of Web-based IdP (SAML, OpenID, and
OAuth2) solutions.

24. Use Cases

25. 25
Business Process – Chip Manufacturer

26. 26
Analytic Models – Prologis

27. Demo
Director, APAC Sales Engineering, Denodo
Chris Day

28. Capabilities

29. 29
Data as a Service (DaaS) using Microservices

30. 30
Capabilities for Data Services
§ Data models (tables, views, stored procedures) available automatically as
web services - zero coding required
§ Available in multiple formats: RESTful (XML, JSON), OData 4, GeoJSON
§ Support for GraphQL: flexible new format for data services
§ Automatic documentation (OpenAPI) and integration with Data Catalog
§ Authentication with modern protocols like OAuth 2.0
§ Authorization based on roles with, including column/row restrictions and
masking
§ Workload management: priorities, quotas (queries per hour), restrictions
by user/role/IP, etc.
§ Caching and query acceleration capabilities
§ Integrates with BPMs, iPaaS and API Management tools
§ Monitoring, access auditing

31. 31
Graph QL
§ Zero code needed to publish a GraphQL interface
§ No n+1 query issue
§ All the power of the Denodo Data Virtualization
engine underneath
§ Advanced query capabilities (optional)
§ Integrated with Denodo's security infrastructure
§ GraphQL-enabled web applications and
frameworks can now talk to Denodo

32. Demo

33. Key Takeaways

34. 34
Key Takeaways
§ Data Virtualization enables reduced time-to-market and improved data
asset utilization via APIs in modern data ecosystems.
§ Decoupling access and storage is a fundamental concept with APIs and Data
as a Service.
§ Real-time is especially important when interacting with business processes
and analytic models.
§ Microservice approaches like REST, OData and GraphQL augment data use.

35. Q&A

36. Next Steps

37. 37
denodo.link/TD2109

38. Featuring Leading Industry Experts
Angel Vina
Founder & CEO
Alberto Pan
Executive VP & CTO
Ravi Shankar
Senior VP & CMO
David Loshin
President of Knowledge Integrity
Terry Moon
Enterprise Information Architect
Logical Data Fabric: The Future of
Data Management and Analytics
Michele Goetz
VP & Principal Analyst
denodo.link/DF21
OCTOBER 13-14
9AM SGT | 12PM AEDT | 6.30AM IST

39. Thanks!
www.denodo.com info@denodo.com
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