Optimize your cloud strategy for machine learning and analytics

1© Cloudera, Inc. All rights reserved.
Optimize your cloud strategy
for machine learning and analytics
Mike Olson
CSO co-founder, Cloudera
James Curtis
Senior analyst, 451 Research

Optimizing Cloud Strategy for ML & Analytics
James Curtis, Senior Analyst, Data Platforms & Analytics

3
451 Research is a leading IT research & advisory company
Founded in 2000
300+ employees, including over 120 analysts
2,000+ clients: Technology & Service providers, corporate
advisory, finance, professional services, and IT decision makers
70,000+ IT professionals, business users and consumers in our research community
Over 52 million data points published each quarter and 4,500+ reports published
each year
3,000+ technology & service providers under coverage
451 Research and its sister company, Uptime Institute, are the two divisions of The
451 Group
Headquartered in New York City, with offices in London, Boston, San Francisco,
Washington DC, Mexico, Costa Rica, Brazil, Spain, UAE, Russia, Taiwan, Singapore
and Malaysia
Research & Data
Advisory
Events
Go 2 Market

4
“Every morning in Africa, an antelope wakes up. It knows it must
outrun the fastest lion, or it will be killed. Every morning in Africa,
a lion wakes up. It knows it must run faster than the antelope, or it
will starve....”

5
“…It doesn’t matter if you’re a lion or an antelope—when the sun
comes up, you’d better be running.”

6
Approximately what percent of your workloads are deployed
in the following environment today? In 2 years?
Source: 451 Research, Voice of the Enterprise:
Workloads and Key Projects, Cloud
Transformation, 2017.

7
Thinking of all applications your organization runs, what
percentage run in which environments? In 2 years?
Key Points
 Cloud deployments will be the
dominant environment in every
category
 Every cloud deployment
environment will see increases
in every workload category
 Analytics and App Development
Development areas expected
strong gains
Source: 451 Research, Voice of the Enterprise:
Workloads and Key Projects, Cloud
Transformation, 2017.

8
8
Drivers for Cloud Adoption
DATA GRAVITY
TRANSFORMATIONAL
CHANGE
IT REJUVENATION
FLEXIBILITY
COST AVOIDANCE

Challenges for Cloud Adoption
9
COST
PEOPLE AND PROCESS
CHANGE
PERFORMANCE
LIABILITY
SECURITY ISSUES
(PERCEIVED AND REAL)

10
Big Data Cloud Segmentation
USER
CSP/VENDOR
Responsibility
Low
High
IaaS PaaS SaaS

11
Big Data Cloud Segmentation
USER
CSP/VENDOR
Responsibility
Low
High
IaaS PaaS SaaS
Depending on the segment, the
responsibilities born by the
user and CSP/vendor vary
considerably.

12
Big Data on Infrastructure-as-a-Service
USER
CSP/VENDOR
IaaS PaaS SaaS
 Provides
cloud
infrastructure
 Configures
environment
 Selects
resources
 Develops
jobs
 Adopts
financial
risk
IaaS
DEPLOYMENT OPTIONS
 Manually deployed
 Marketplace image
 On bare metal
WHEN IT MAKES SENSE
 Control over the
environment is required
 Customized or specialized
use cases
 Procurement is a barrier

13
Big Data on Infrastructure-as-a-Service + PLUS
USER
CSP/VENDOR
IaaS PaaS SaaS
 Automated tools
config/resources
 Provides cloud
infrastructure
 Aided/Configures
environment
 Aided/Selects
resources
 Develops
jobs
 Adopts
financial
risk
IaaS PLUS
DEPLOYMENT OPTIONS
 CSP/vendor-specific
tools for deploying and
configuring the
environment
WHEN IT MAKES SENSE
 Control over the
environment is required
 Customized or specialized
use cases
 Procurement is a barrier

14
Big Data-as-a-Service
USER
CSP/VENDOR
IaaS PaaS SaaS
 Automated tools
config/resources
 Provides cloud
infrastructure
environment
 Aided/Selects
resources
 Develops
jobs
 Adopts
financial
risk
IaaS PLUS
AVAILABILITY
 By CSP (single cloud)
 By vendor that leverages
cloud infrastructure on
behalf of user
 Configures
environment
 Develops
jobs
 Adopts
financial
risk
 Provides
cloud
infrastructure
 Masks
complexity
-as-a-Service
WHEN IT MAKES SENSE
 Full control not required
 Limited resources or don’t
want responsibility for
certain tasks
 Alignment with a service
provider

15
Managed Big Data Services
USER
CSP/VENDOR
IaaS PaaS SaaS
 Automated tools
config/resources
 Provides cloud
infrastructure
environment
 Aided/Selects
resources
 Develops
jobs
 Adopts
financial
risk
IaaS PLUS
 Configures
environment
 Develops
jobs
 Adopts
financial
risk
 Provides
cloud
infrastructure
 Masks
complexity
-as-a-Service
 Develops
jobs and
manages
workloads
 Provides
cloud
infrastructure
 Masks
complexity
 Configures
 Adopts
financial risk
Managed Service
CONSIDERATIONS
 Processing engines,
features, and capabilities
can vary
 Professional services
optional
 Pricing varies
WHEN IT MAKES SENSE
 Focus is on the job instead
of infrastructure
 The ‘managed’ services
serve the organization
 Resources are not available
or organization not willing
to invest is in-house skills

16
Managed Big Data Services
USER
CSP/VENDOR
IaaS PaaS SaaS
 Automated tools
config/resources
 Provides cloud
infrastructure
environment
 Aided/Selects
resources
 Develops
jobs
 Adopts
financial
risk
IaaS PLUS
 Configures
environment
 Develops
jobs
 Adopts
financial
risk
 Provides
cloud
infrastructure
 Masks
complexity
 Develops
jobs and
manages
workloads
 Perform data
processing
and analysis
 Provides
cloud
infrastructure
 Masks
complexity
 Configures
 Adopts
financial risk
 Provides
cloud
infrastructure
 Masks
complexity
 Configures
environment
 Adopts
financial risk
-as-a-Service Managed Service Managed Proc. WHERE ARE WE HEADED?
 Processing frameworks,
engines, databases do not
matter to organizations
 Automation, advanced
methods leveraging machine
learning will be integrated
 Focus on an ‘outcome’
desired by the organization
 User base can be expanded
as complexity is abstracted
out of the system

17
Why not just use HDFS, either on Amazon EC2 or Azure VMs or via Hadoop as a cloud
service?
Big data analytics in the cloud
Vs

18
Why not just use HDFS, either on Amazon EC2 or Azure VMs or via Hadoop as a
cloud service?
Cost
• It costs significantly less to store data in S3 (to use AWS as an
example) than HDFS running on EC2
• HDFS requires storing three copies of each block of data for
resiliency
• S3 offers automated backups and file compression
• Users only pay for the compute resources they consume as
and when they analyze the data.

19
cloud service?
Scalability
• HDFS relies on local storage
• HDFS in the cloud requires manual configuration and
management of associated storage.
• Cloud storage is designed to automatically scale as more data
is added, without any direct user involvement.

20
cloud service?
Durability and persistence
• Data is persisted in EC2 storage instances only for the life of the
instance itself, whereas data is always persisted in S3.
• If you’re running HDFS on EC2, it’s highly likely that you’ll be
storing the data in a persistent data store like S3 anyway and
moving it to and fro for the purposes of analysis.
• S3 is also designed to deliver durability of 99.999999999%,
which would be hard for even the most highly skilled Hadoop
administrator to match.

21
ARTIFICIAL INTELLIGENCE
The quest to build software running on
machines that can ‘think’ and act like
humans
MACHINE LEARNING
A subset of artificial intelligence focused on
using algorithms that learn and improve
without being explicitly programmed to do
so
DEEP LEARNING
A branch of machine learning based on
specific set of algorithms that attempt to
mimic the human brain in the form of multi-
layered neural networks

ML and the Cloud
Success in ML depends on a
combination of data, algorithms, skills
and compute resources.
While the public cloud is by no means
essential for ML, low-cost storage and
compute services enable storing and
processing data at larger volumes.
Deep learning – which typically
involves modeling many layers of
neural networks and, thus, is highly
resource-intensive – particularly
benefits from recent computing
advancements and increasing comfort
levels with the cloud. 22

• Organizations succeed when they match their cloud requirements with their
resources in selecting their cloud deployment preferences, thus enabling the
strengths of the user and CSP/vendor.
• The journey to the cloud requires significant planning, but the goal remains the
same and that is to better manage and leverage the data. The cloud is a means
to that end.
• Carrying out analytics in the cloud works best when organizations utilize the
infrastructure advantages of the cloud such as the ability to scale and secure
large amounts of compute and storage, especially for ML.
Some final thoughts to consider
23

24
Thank you
james.curtis@451research.com
@jmscrts
www.451research.com

Cloudera & The Cloud
Mike Olson
CSO co-founder, Cloudera

My organization
is moving to the cloud,
why should we
consider Cloudera?

Current State

Instant, self-service access to
data and IT resources
Application performance
Job-oriented tools
Choice and integration
Secure, controlled provisioning
of data and IT resources
Predictable infrastructure costs
Systems-oriented tools
Standardization and portability
KNOWLEDGE WORKERS INFRASTRUCTURE
TEAM
Stakeholders

–+
• Speed of deployment
• Tenant isolation
• Self-service
• Workload elasticity
• Shared storage
• Pay-as-you-go
• Bring your own tools
• Bring your own data
• Powerful network
• Proliferation of data copies
• Multiple security frameworks
• Difficult to troubleshoot workloads
• No shared metadata
• Unable to track data lineage
• Disjointed services
• Few on-premise integration services
• Proprietary services
• Cloud lock-in
CLOUD
BENEFITS
CLOUD
SETBACK
S

Deployment Options
ON-
PREMISE
CLOUD
INFRASTRUCTURE SERVICES
PRIVATE CLOUDBARE METAL

Deployment model choices
Bare Metal Private Cloud IaaS PaaS
Applications Applications Applications Applications
Clusters Clusters Clusters Clusters
Operating System Operating System Operating System Operating System
Network Network Network Network
Storage Storage Storage Storage
Servers Servers Servers Servers
Customer managed Vendor managed

Traditional applications
32
Data
Exploration
STORAGE
SECURITY
GOVERNANCE
WORKLOAD MGMT
INGEST &
REPLICATION
DATA CATALOG
SQL & BI
Analytics
STORAGE
SECURITY
GOVERNANCE
WORKLOAD MGMT
INGEST & REPLICATION
DATA CATALOG
Operational
Real-Time DB
STORAGE
SECURITY
GOVERNANCE
WORKLOAD MGMT
DATA CATALOG
ETL & Data
Processing
STORAGE
SECURITY
GOVERNANCE
WORKLOAD MGMT
INGEST &
REPLICATION
DATA CATALOG
Custom
Functions
STORAGE
SECURITY
GOVERNANCE
WORKLOAD MGMT
DATA CATALOG
Many data silos, each with its own proprietary tools and infrastructure
Different vendors, products, and services on-premises versus in cloud
A fragmented approach is difficult, expensive, and risky

The Answer

● The modern platform for machine
learning and analytics
● with multiple deployment options
● and one shared data experience

One platform. Multiple workloads
DATA ENGINEERING OPERATIONAL
DATABASE
ANALYTIC DATABASE DATA
SCIENCE
DATA PROCESSING
• Cost efficient
• Reliable
• Scalable
• Based on Spark,
MapReduce, Hive
& Pig
• Supported by
Workload
Analytics
FAST BI & SQL
• Flexibilty
• Elastic scale
• Go beyond SQL
• Based on
Impala & Hive
• SQL dev enviro
• Supported by
Workload
Analytics
MACHINE LEARNING
• Fast dev to
production
• Secure self-serve
• Based on
Python, R, and
Spark
• ML dev
environment
(CDSW)
ONLINE & REAL-TIME
• High throughput,
low latency
• Strongly consistent
• Based on
Hbase, Kudu
& Spark
streaming

Multiple deployment options
OPERATIONAL
DATABASE
DATA
SCIENCE
ANALYTIC
DATABASE
DATA
ENGINEERING
DATA
ENGINEERING
ANALYTIC
DATABASE
PRIVATE CLOUD
BARE METAL INFRASTRUCTURE SERVICES
(in beta soon)

• Shared catalog
• Unified security
• Consistent governance
• Easy workload management
• Flexible ingest and replication
Open platform services
Built for multi-function analytics | Optimized for cloud

38© Cloudera, Inc. All rights reserved. 38
The modern platform for machine learning and analytics optimized for the cloud
DATA CATALOG
SECURITY GOVERNANCE
WORKLOAD
MANAGEMENT
INGEST &
REPLICATION
EXTENSIBLE
SERVICES
CORE SERVICES
DATA
ENGINEERING
OPERATIONAL
DATABASE
ANALYTIC
DATABASE
DATA
SCIENCE
S3 ADLS HDFS KUDU
STORAGE
SERVICES
Cloudera Enterprise
PRIVATE CLOUDBARE METAL INFRASTRUCTURE
DEPLOYMENT
OPTIONS SERVICES

Run anywhere. Deploy any way.
Simple Unified Enterprise
Proven at scale
Trusted security
Hybrid or multi cloud
Platform-as-a-Service
Simplifies operations
Works with your tools

"Better to bet on cloud providers
for infrastructure, Cloudera for data,
compute and security fabric, and
leave the rest to the ecosystem"
--- Sean Owen, Director, Data Science at Cloudera

Thank you
Mike Olson
@mikeolson

Optimize your cloud strategy for machine learning and analytics

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