Analytics in the Cloud: Getting The Most Out Of Analytics Deployments
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Transcript of Analytics in the Cloud: Getting The Most Out Of Analytics Deployments
Lyndsay Wise, Research
Director, EMA
Jeff Kelly, Data Evangelist,
Pivotal
Ian Andrews, VP of
Products, Pivotal
Today’s Speakers
Submit your questions to the panel using:
@wiseAnalytics @jeffreyfkelly #cloudAnalytics
© 2016, Enterprise Management Associates
Join the Conversation
• Business Drivers and Perception of
Cloud Analytics
• Financial Drivers and TCO
• Business Agility and Smart Applications
• Cloud Analytics Success factors
• Cloud Analytics Use Cases
• Summary & Q&A
Agenda for Today’s Webinar
Cloud is better They are the
same
On-premises is
better
Ease of Adoption 47.1% 43.3% 9.6%
Ease of Technical Distribution 48.6% 38.9% 12.5%
Functionality 45.7% 44.2% 10.1%
Time to Implementation 47.6% 41.3% 11.1%
Total Cost of Ownership 53.4% 35.6% 11.1%
Perceptions of Cloud vs. On-Premises Use
Infrastructure1
Platform2
Software3
A SEPARATION OF CONCERNS
Modern cloud platforms enable software to
be developed & deployed without regard
to the underlying infrastructure
Modern Software Methodology Modern Cloud Platform
BEING A CLOUD NATIVE COMPANY
Is about building high-quality software—at start-up speed—
leveraging a modern cloud platform, a modern development
process, and the power of Big Data to continuously drive
innovation
Big Data
Stream + Batch Processing
Programming + Operating Model
Cloud-Native Platform
Microservices FrameworkPlatform RuntimeHadoop
DW
Spark
Microservices and Polyglot Persistence
IMDG
K/V Store
Relational DB
Big Data &
Machine Learning
Modern Cloud-Native Data Architecture
Cloud Infrastructure
18.6%Better information
visibility/higher
efficiencies
16.6%Improved speed to
implementation
Top Business Drivers
19.0%Time savings
18.4%ROI
17.0%Effective use of
biz resources
Delivering Value Through Cloud – Moving
Towards Revenue Growth
Great software companies leverage Big Data
to fundamentally change the consumer
experience and pioneer entirely new
business models
DISRUPTIVE AND CONVERGING TRENDS IN BIG DATA
(Data)
Microservices
Loosely coupled
services architecture,
bounded by context
Cloud-Native
Platforms
Enabling continuous
delivery & automated
operations
Open Source
Database
Innovation
Extreme scale &
performance advantages,
built for the cloud
Machine
Learning
Use of predictive
analytics to build
smart apps
Scale-out
analytic
database
Model API Cloud Native
Application
Platform
Data
Sources
0 5
Smart Apps: Models Manifesting as Microservices
64.8%Successful
3.3 %Not successful
13.7%Neither successful,
nor unsuccessful
Success Levels of Cloud Deployments
AppDevelopment
Data analytics
Cloud-nativeApp platform
Data Science & Model building
DataMicroservice
AP
P Must support scale-out
query processingMust deliver as an API
Must embrace agile development,
focus on outcomes
Must support
microservices, agile dev, and
connect to big data analytics
A Real-World Example
http://enterprisemanagement.com/ http://springoneplatform.io/
More resources at pivotal.io/resources and https://blog.pivotal.io/
Resources