This document discusses the growing trend of organizations adopting hybrid cloud data architectures and strategies. Some key points: - Many organizations are moving analytics workloads and data to the cloud while still maintaining some data and systems on-premises, resulting in hybrid environments. - A logical data fabric with data virtualization at its core can help organizations address challenges of hybrid cloud architectures like integrating data across environments and platforms, automating tasks, and improving analytics performance. - Capabilities like data discovery, analyzing both data at rest and in motion, and cataloging all data assets are important for a logical data fabric in hybrid cloud environments.

1. Logical Data Fabric: The Future of Data
Management and Analytics

2. #DenodoDataFest
Delivering Faster Insights with a
Logical Data Fabric in a Hybrid Cloud World
President, Knowledge Integrity, Inc.
Program Director, Master of Information
Management, University of Maryland
David Loshin

3. Migrating to the Cloud
• The cloud is becoming a
popular choice for reporting, BI,
and analytics applications
• According to a recent TDWI
survey:
• 46% of respondents indicated
their organization was already in
the cloud for analytics
• Another 34% of respondents
indicated their organization was
planning to move into the cloud
• Cloud environments provide a
flexible and scalable
environment for analytics

4. Cloud Data Architecture is Becoming the Norm
• Growing numbers of
individuals and their
organizations are adopting
cloud data strategies
• There are growing concerns
about the
viability/sustainability of
traditional on-premises data
environments
• However, continuing
migration to the cloud does
not mean that on-prem
systems are being
immediately abandoned

5. The Hybrid Information Environment
• Trend towards
modernization and
migration to the
cloud
• Incorporation of a
variety of platform
alternatives into a
Hybrid Enterprise
Data Lake
Hosted/Clouds
Streaming
Data
Sources
Data Warehouse
Data Warehouse
On-premises
Shared
Data
Sources
Data Warehouse

6. CSP Choice & Multicloud Environments
• Hybrid solutions spanning
multiple cloud hosts
– Help meet reporting and
analytics
– Leverage best of breed
services
– Distribute risk
– Balance costs
• TDWI sees organizations that
have opted for more than one
cloud vendor

7. Cloud Data Architectures: Consumers are Not Satisfied
• Highest level of dissatisfaction is with the time it takes to
load data to the cloud
• Third-highest level of dissatisfaction is with support for data
streaming to the cloud

8. Emerging Concerns and Complications…
• Businesses that naively migrate
to the cloud may not completely
benefit from a cloud computing
strategy
• Concerns remain about:
– Cost management
– Data security
– Overall governance
“managing costs is the most common
challenge organizations face when trying to
augment or replace existing on-premises
systems with cloud-based platforms and
services for BI, analytics, data integration,
and data management (57% of survey
respondents).”
“half of those surveyed (47%) say that the
issues of data security, identity
management, and access authentication
form a major challenge.”
The notion of a data fabric is becoming important as organizations put more data
in multiple cloud-based storage platforms

9. Multicloud CSPs
The Logical Data Fabric
Businesses can address uncertainties using a logical data fabric, with data virtualization at its core, to
seamlessly overcome challenges and simplify reporting and analytics
The most critical capabilities of a logical data fabric are:
Data
Warehouse
Data
Warehouse
On-Premises Data
Partner Data
Sources
Extra-Enterprise
Data
IoT and
Streaming Data
1. Integrate data across multicloud environments
2. Automate manual tasks using augmented intelligence
3. Boost performance of analytics with rapid data delivery
4. Support data discovery and data science initiatives
5. Analyze across data at rest and data in motion
6. Catalog all data for discovery, lineage, and associations

10. #1 Integrate data across multicloud environments
• Mostly structured data
• Scope includes on-premises
transaction and analytical
systems
• Less-structured data organization
• Scope of data includes migrated and
acquired data
• Multiple cloud instances employed
• Semi- and unstructured data
• Scope of data includes acquired
and streamed data
• Different cloud hosts may
support ingest and integration
better than others
• A hybrid array of on-prem + multiple cloud deployments establishes a “data fabric” that spans the
cloud host environments
• Look for products that leverage data virtualization capabilities to
• Access and aggregate data within each environment
• Coordinate access across the different environments
Multicloud CSPs
Data
Warehouse
Data
Warehouse
On-Premises Data
Partner Data
Sources
Extra-Enterprise
Data
IoT and
Streaming Data

11. #2 Automate manual tasks using augmented intelligence
• Operating within a multicloud environment
runs certain risk/challenges:
– Data awareness and availability
– The need for seamless continuity
– Expectation of performance
• An enterprise-grade logical data fabric
must automatically address these issues
• Use machine learning to
– Analyze data consumer usage patterns
– Develop models for data asset
recommendation
– Anticipate access demands and modulate
query requests
– Use intelligent caching to meet performance
demands
71% of respondents to a recent
TDWI survey on analytics stated
that demand for machine
learning is increasing

12. #3 Boost performance of analytics with rapid data delivery
• Recent TDWI Research
surveys show that
– 80% of respondents say that it
is important to have solutions,
cloud services, and practices to
enable faster analytics
– 39% indicated that faster
analytics was “extremely
important”
– 77% of organizations say that
“near or true real-time data, BI
dashboards, and analytics are
important to their firm’s
success”
– 30% say that near or true
real-time is “very important”
• Speed access, reduce latency, and optimize
queries across the hybrid enterprise:
– Pushdown optimization
– Caching
– Data movement/shipping
• Blend dynamic query optimization with
leveraging massive parallel processing and
in-memory data management

13. #4 Support data discovery and data science initiatives
• TDWI surveys indicate growing interest in data science
– 68% of surveyed organizations said they had already hired data scientists
– 22% reported that they were planning to hire some to help advance their analytics initiatives
• Four fundamental data management capabilities must be supported by the logical data fabric:
Organizationa
l data
awareness
Democratized
data
availability
Data model
flexibility
Transparent
accessibility

14. #5 Analyze across data at rest and data in motion
• Most traditional BI/Analytics
involves data at rest
• The virtually unbounded storage
capacity and ability to integrate
stream processing that clouds
provide lowers the barrier for
processing data in motion
• Analyzing both enables
integration of analytical models
into the data ecosystem
• Look for a logical data fabric that
can support integration and use
of both data at rest and data in
motion across that multicloud
environment
Data at rest Data in motion

15. #6 Catalog all data for discovery, lineage, and associations
• The rise of multicloud data
environments runs the risk of
ungoverned data migrations
• Uncontrolled movement of different
types of data assets to the cloud can
lead to confusion and difficulty in
finding the right data sources for
analysis
• Data catalogs provide an inventory of
enterprise data assets to support
– Data discovery
– Management of data lineage
– Associations among different data assets
across different platforms

16. Considerations
• Cloud data and application migration and
modernization does not happen overnight
• Cloud deployments are bound to coexist with
on-premises application for the short- and medium-term
• The complexity of hybrid cloud data architecture will
confound analytics consumers without the right
complement of tools and processes

17. Data Virtualization Actualizes the Logical Data Fabric
• Data virtualization techniques helps enable data democratization by
managing data assets distributed across a hybrid multicloud environment
– Supports access, management, and BI/analytics across disparate platforms
– Enhances emerging uses cases that leverage AI/ML
– Automates data asset discovery, assessment, classification, and cataloging
– Incorporates pushdown optimization and caching to speed response times
– Allows analysis of both data at rest and data in motion
• Use a logical data fabric with data virtualization as the foundation for
actualizing the hybrid cloud data architecture to:
– Optimize data movement
– Streamline data pipeline orchestration
– Reduce or eliminate data latency
– Speed delivery for faster analytics

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