With billions of new devices, IoT is transforming how businesses capitalize on data. Data driven organizations are using IoT as as a means to improve their customer experience, drive operational efficiencies, and enable new business models. However, without the right data management strategy and tools, investments in IoT can yield limited results. Join Cloudera and 451 Research for a joint webinar to learn more about some of the data management best practices and how organizations are using advanced analytics and machine learning to enable IoT use cases.

1. 1© Cloudera, Inc. All rights reserved.
- Trends, Best Practices and Key Use Cases
IoT Data Management
WEBINAR

2. 2© Cloudera, Inc. All rights reserved.
Your Speakers for Today…
Vijay Raja
Solutions Marketing Lead, IoT
Christian Renaud
Research Director, Internet of Things

4. Number of Current
and Planned Enterprise
IoT Initiatives
IoT Respondents
4
Q. How many IoT initiatives
does your organization have in
the following phases of
implementation? (Mean) n=346
Source: 451 Research, Voice of the
Enterprise: Internet of Things, Vendor
Evaluations 2016
Current State of IoT Adoption

5. 5

6. Key Use Cases Gaining Traction Today
611%
12%
13%
15%
22%
39%
50%
74%
Smart Grid
Smart City
Health/Patient Monitoring
Retail/Point-of-Sale
Environmental Monitoring (Weather)
Mobile Device Tracking
Surveillance/Security
Management/Automation (Fleet, Factory, Supply Chain)
Source: 451 Research, Voice of the Enterprise:
Internet Of Things, Budgets and Outlook
2016: “Which of the following best describes
the IoT/projects enabled by these connected
endpoints?”. Base: IoT-familiar respondents.
Multiple select.

7. 7
By Morio - Own work, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=9951305
A typical Formula One car already
carries between 150 and 300 sensors
Copyright (C) 2016 451 Research LLC

8. 8
By Morio - Own work, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=9951305
Today, those couple of hundred sensors
already capture data in milliseconds.
Race cars generate 100-200Kb of data
per second
Copyright (C) 2016 451 Research LLC

9. 9
By Morio - Own work, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=9951305
Each individual reading might translate
into a relatively small amount of data
but there are hundreds or thousands of
them being generated each second
Copyright (C) 2016 451 Research LLC

10. What is the Data of Things?
10
• Metrics and measures (Metadata and State).
This type of data consists of the data that comes from the ‘things’ themselves – measures from
sensors such as temperature, humidity, acceleration, vibration, speed, video feeds, biometric
data, and so on.
• Transactions (Commands).
They could include an interaction between two machines, or between a system and a human
being. They could include an adjustment to the parameters of a machine or system, such as an
alteration to a generator or air conditioning unit.
• Diagnostics (Telemetry).
Provides an insight into the overall health of a machine, system or process. Diagnostic data might
not only show the overall health of a system, but also serve as an alert that a system is no longer
functioning within normal parameters and might need further analysis to determine the root
cause.

11. 11
IoT Data – Unique Attributes
Frequency of interaction
Volume of
data (per
interaction)
Traditional
Enterprise
applications
IoT
• Traditionally, most transactional systems were designed to be able to
cope with one or two transactions every few minutes – at the most
• A sensor or smart device could potentially generate data that needs
to be handled by backend systems in some way every millisecond.
• Each individual reading from a sensor might translate into a relatively
small amount of data

12. 12
IoT Data – Unique Attributes
Frequency of interaction
Volume
of data
(in total)
Traditional
Enterprise
applications
IoT
• Each individual reading from a sensor might translate into a relatively
small amount of data, but there are hundreds or thousands being
generated each second.

13. IoT Data Processing Requirements
In order to gain insight and value from data generated by the IoT, enterprises need to:
13
Capture and
process data
coming from
sensors and
other devices
Ensure
interoperability
of data coming
from multiple
sensors with
multiple data
formats and
multiple
protocols
Analyze data in
real-time to
compare it with
historical trends
Ensure that
appropriate
responses are
built in to
operational
application
workflows and
business
processes
CAPTURE INTEROPERATE ANALYZE ACT

14. INTERNET OF THINGS:
ORGANIZATIONAL DYNAMICS
2016
Source: 451 Research, Voice of the Enterprise:
Internet of Things, Budgets and Outlook 2016
Q41. Which of the following
technologies or processes
are high priorities for your
organization to deploy in
2017 for your Internet-of-
Things (IoT) initiatives?
14
47.0%
33.7%
31.2%
30.1%
26.5%
22.6%
19.4%
18.3%
14.0%
3.9%
14.3%
IoT Security
Big Data Analytics for IoT
IoT Infrastructure Equipment
IoT Applications
IoT Network Edge
IT Staff To Support IoT
IoT Storage
Aligning Corporate Policies, Procedures and Compliance To Support
IoT
Aligning IoT Across Multiple IT Groups
Other
None
Percent of Sample
n = 279
High-Priority Technologies
and Processes for IoT
Initiatives
IoT-Familiar Respondents
Security & Analytics – High Priority Areas for IoT
Adoption

15. INTERNET OF THINGS:
ORGANIZATIONAL DYNAMICS
2016
Source: 451 Research, Voice of the Enterprise:
Internet of Things, Budgets and Outlook 2016
Q29. Which skills or
capabilities will these new
IoT staff need?
15
69.9%
54.8%
54.8%
45.2%
41.1%
38.4%
35.6%
28.8%
24.7%
20.5%
19.2%
1.4%
Data Analytics
Security
Cloud Computing
Network Edge/Perimeter
Software Development
Virtualization
Standards and Protocols
Storage Management
General Management
Compliance/Licensing
Distributed Computing
Other
Percent of Sample
n = 73
Required Skills for New IoT
Staff
Respondents Adding Dedicated IoT Staff
Big Data Analytics will be a Critical Success
Factor for IoT

16. 4
High-level IoT Data Architecture

17. Dramatic workload migration
over the next two years: from
41% currently to 60%
expected in two years
On-premises to off-premises
shift: from 35% to 52%
Significant expansion of
public clouds (IaaS and SaaS)
as workload execution
venues
Source: 451 Research, Voice of the Enterprise: Cloud
Transformation, Workloads & Key Projects 2016
51.6%
33.5%
7.4%
7.0%
13.8%
14.2%
7.8%
11.1%
5.5%
11.7%
13.8%
22.5%
2016 2018
Software-as-a-Service (SaaS)
Infrastructure-as-a-Service (IaaS)
Hosted Private Cloud
On-Premises Private Cloud
Off-Premises Non-Cloud
On-Premises Non-Cloud
2016 2018
IT Workload Migration
17

18. INTERNET OF THINGS:
ORGANIZATIONAL DYNAMICS
2016
Source: 451 Research, Voice of the Enterprise:
Internet of Things, Budgets and Outlook 2016
Q37. Which deployment
locations do you plan to use
to store and analyze IoT data
in 2017?
18
58.0%
37.2%
34.4%
28.1%
22.9%
17.0%
Company-Owned/Leased Datacenter Facilities
IT Infrastructure Located Where The IoT Data Is Generated
Public Cloud Infrastructure (IaaS, PaaS)
Software-as-a-Service (SaaS)
Managed Services/Hosted Services
Third-Party Colocation Facilities
Percent of Sample
n = 288
Deployment Locations
Planned for 2017
IoT-Familiar Respondents
On Prem/ Datacenter still the epicenter for Data
Analytics

19. Infrastructure on-premise
fastest growing category for
IoT workload analytics
SaaS growing rapidly y/y
Public cloud and hosted
services experiencing strong
y/y uptake by nascent IoT
verticals
Source: 451 Research, Voice of the Enterprise: Internet of
Things, Budgets and Outlooks, 2016 (Multi-answer)
57.7% 58.0%
18.9% 17.0%
28.8% 37.2%
18.0%
22.9%
32.4%
34.4%
20.7%
28.1%
2016 2017
Software-as-a-Service (SaaS)
Infrastructure-as-a-Service (IaaS)
Hosted Private Cloud
Infrastructure on-premises
Colocation
On-Premises Non-Cloud
2016 2017
IoT Workload Migration
19

20. 20
IoT Analytics Continuum – Edge, Near Edge, Cloud

21. 21
Manufacturing
Optimize
Operations
Reduce
Risk
New/
Enhanced
Existing
Products/
Services
Customer
Targeting/
Increase
Sales
80%
69%
45%
22%
Source: 451 Research, Voice of the
Enterprise IoT Budgets and Outlook 2016

22. 22
Transportation
93%
47%
87%
27%
Optimize
Operations
Reduce
Risk
New/
Enhanced
Existing
Products/
Services
Customer
Targeting/
Increase
Sales
Source: 451 Research, Voice of the
Enterprise IoT Budgets and Outlook 2016

23. 23
Utilities
Optimize
Operations
Reduce
Risk
New/
Enhanced
Existing
Products/
Services
Customer
Targeting/
Increase
Sales
86% 81%
48%
23%
Source: 451 Research, Voice of the
Enterprise IoT Budgets and Outlook 2016

24. 24© Cloudera, Inc. All rights reserved.
IoT Data Characteristics
- The Foundation of Hadoop’s Potential
IoT data comes from a variety of different sources
Massive volumes of intermittent data streams
Generated from a variety of data sources
Predominantly time-series
Can come in streams (real-time) or batches
Diverse data structures and schemas
Some of it may be perishable
Combining sensor data with contextual data is the
key to value creation from IoT

25. 25© Cloudera, Inc. All rights reserved.
The IoT Ecosystem & Architecture
IoT Gateway
Gateway
• Edge-Processing
• Edge-Analytics
IoT Data Storage, Processing & Analytics
Centralized IoT Analytics
• Time Series Data, Trends
• Machine Learning
• Context Enrichment
• Deeper business insights
Distributed Data
Processing &
Analytics
• Cloud & On-
Premise
Connected Things
• Analytics at the edge
• For immediate
response
Data Center
Cloud
IoT Analytics
Enterprise Data
Sources

26. 26© Cloudera, Inc. All rights reserved.
A Platform That Just Won’t Stop Growing…
NEW PROJECTS
EXISTING PROJECTS
*CDH SUPPORTED
Core Hadoop
(HDFS,
MapReduce)
Solr
Pig
Core Hadoop
HBase
ZooKeeper
Solr
Pig
Core Hadoop
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
Core Hadoop
Sqoop
Avro
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
Core Hadoop
Flume
Bigtop
Oozie
HCatalog
Hue
Sqoop
Avro
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
YARN
Core Hadoop
Spark
Tez
Impala
Kafka
Drill
Flume
Bigtop
Oozie
HCatalog
Hue
Sqoop
Avro
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
YARN
Core Hadoop
Parquet
Sentry
Spark
Tez
Impala
Kafka
Drill
Flume
Bigtop
Oozie
HCatalog
Hue
Sqoop
Avro
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
YARN
Core Hadoop
Knox
Flink
Parquet
Sentry
Spark
Tez
Impala
Kafka
Drill
Flume
Bigtop
Oozie
HCatalog
Hue
Sqoop
Avro
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
YARN
Core Hadoop
Kudu*
RecordService*
Ibis*
Falcon
Knox
Flink
Parquet*
Sentry*
Spark*
Tez
Impala*
Kafka*
Drill
Flume*
Bigtop*
Oozie*
Hcatalog*
Hue*
Sqoop*
Avro*
Hive*
Mahout*
Hbase*
ZooKeeper*
Solr*
Pig*
YARN*
Core Hadoop*
2006 2008 2009 2010 2011 2012 20132007 2014 Present

27. 27© Cloudera, Inc. All rights reserved.
Cloudera Enterprise – The Data Mgmt. Platform for IoT
Connected
Devices/ IoT Data
Sources
Enterprise Data
Sources
External Data
Sources
BI Solutions Real-Time
Apps
Search Data Science
Workbench
SQL
Machine
Learning
Data Center
Hybrid
Cloud
Sensor/ IoT Data
• Data Storage
• Data Processing
• Machine Learning
• Real-time Analytics
OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Encrypt and KeyTrustee
Optimizer
BATCH
Sqoop
REAL-TIME
Kafka, Flume
PROCESS, ANALYZE, SERVE
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
Sentry, RecordService
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
SDK
Partners
Other Enterprise
Data Sources

28. 28© Cloudera, Inc. All rights reserved.
The Cloudera Platform for IoT – Data Mgmt. Value Chain
Data Sources Data Ingest Data Storage & Processing
Serving, Analytics &
Machine Learning
ENTERPRISE DATA
HUB
Apache Kafka
Stream or batch ingestion of IoT data
Apache Sqoop
Ingestion of data from relational
sources
Apache Hadoop
Storage (HDFS) & deep batch
processing
Apache Kudu
Storage & serving for fast changing
data
Apache HBase
NoSQL data store for real time
applications
Apache Impala
MPP SQL for fast analytics
Cloudera Search
Real time searchConnected Things/ Data
Sources
Structured Data Sources Security, Scalability & Easy Management
Deployment Flexibility:
Datacenter Cloud
Apache Spark
Stream & iterative processing, ML

29. 29© Cloudera, Inc. All rights reserved.
HDFS
Fast Scans,
Analytics
and Processing of
Stored Data
Fast On-Line
Updates &
Data Serving
Arbitrary
Storage
(Active Archive)
Fast Analytics
(on fast-changing or
frequently-updated data)
Kudu – Fast Analytics on Fast Data
Real Time Use cases that fall between HDFS and HBase were difficult to manage
Unchanging
Fast Changing
Frequent Updates
HBase
Append-Only
Real-Time
Complex Hybrid
Architectures
Analytic
Gap
Pace of Analysis
PaceofData

30. 30© Cloudera, Inc. All rights reserved.
Cloudera for IoT – Key Enabling Capabilities
Ideal for real-time analytics on
IoT and time series data.
Simplifies Lambda architectures
for running real-time analytics
on streaming data
Preserve business flexibility and
data portability and minimize
cloud lock-in by running in any
one of the three major public
cloud providers or in private cloud
Kudu: Real-Time Analytics Multi-Cloud Portability Data Science Workbench
Collaborative hub for enterprise
data science and an integrated
development environment for
running Python, R, & Scala with
support for Spark

31. 31© Cloudera, Inc. All rights reserved.
IoT - Key Customer Use Cases

32. 32© Cloudera, Inc. All rights reserved.
Powering a Variety of IoT Use Cases…
Connected Vehicles
Usage Based Insurance
Industrial IoT
Predictive Maintenance
Smart Cities & Ports Oil & Gas
Aerospace & Aviation Smart Healthcare

33. 33© Cloudera, Inc. All rights reserved.
Using Predictive Maintenance to
Improve Performance and Reduce Fleet
Downtime
• Real-time visibility of 300,000+ trucks
in order to improve uptime and vehicle
performance
• OnCommand Connection is collecting
telematics and geolocation data across
the fleet
• Reduced maintenance costs to $.03
per mile from $.12-$.15 per mile
• Centralizing data from 13 systems with
varying frequency and semantic
definitions
TRANSPORTATION
» PREDICTIVE MAINTENANCE
» IMPROVED SERVICE
» DATA DRIVEN PRODUCTS
IOT &
Connected
Products
CASE STUDY

34. 34© Cloudera, Inc. All rights reserved.
Predictive Maintenance on industrial-
grade turbines for hydro power stations
Challenge:
• Gather, store and analyze noise levels
from turbines for anomaly detection
Solution:
• Cloudera platform used to gather and
analyze acoustic data/audio files
coming from the turbines in real-time
• Diagnostic solution to monitor the
health of turbines and predict failures
in advance
• Prevent downtimes and failures
PREDICTIVE MAINTENANCE
» INDUSTRIAL IoT
» LOWERED DOWNTIME
» LOWERED COSTS
Predictive Maintenance - Turbines
DATA-DRIVEN
PROCESS
CASE STUDY
IOT &
Connected
Products

35. 35© Cloudera, Inc. All rights reserved.
#1 Telematics provider with 130 billion
miles of driving data collected from black
boxes in connected cars
Challenge:
• Drive analytics on 12 million miles of
driving data collected every hour
Solution:
• Telematics solution based on Cloudera to
process data from black boxes
• Analytics around driving behavior, risks,
location, braking patterns, contextual
elements and crash information
• Provide Usage Based Insurance services
TELEMATICS
» CONNECTED VEHICLES
» INSURANCE TELEMATICS
» PREDICTIVE ANALYTICS
Connected Car Telematics for Insurance
CASE STUDY
DATA-DRIVEN
PROCESS
IOT &
Connected
Products

36. 36© Cloudera, Inc. All rights reserved.
Ensuring Zero Down Time & lowered
energy costs on industrial-grade robots
Challenge:
• Gather, store and analyze sensor data
from 10,000 robots in order to minimize
downtime
Solution:
• Cloudera platform used to gather and
analyze sensor data coming from
robots in real-time
• Diagnostic solution predicts potential
failures and alerts the operators in
advance
ZERO DOWN TIME
» INDUSTRIAL IoT
» LOWERED DOWNTIME
» LOWERED COSTS
Zero Down Time – Industrial Robotics
DATA-DRIVEN
PROCESS
CASE STUDY
DATA-DRIVEN
PRODUCTS

37. 37© Cloudera, Inc. All rights reserved.
Enabling the State of Kentucky
optimize management of snow and ice
events in real time
Challenge:
• Needed more efficient approach to
inclement weather road management
Solution:
• Real-time weather response system
that incorporates real-time data from
Waze, HERE, ESRI’s GeoEvent
processor, and Automatic Vehicle
Locations (sensor data from salt
trucks).
• KYTC aggregates 15-20 million records
every day and process more than a
million records per second.
Smart Cities
2016 Data Impact Award
Winner
State of Kentucky Department
of Transportation
CASE STUDY

38. 38© Cloudera, Inc. All rights reserved.
Using sensors & IoT to improve efficiencies in
cargo handling
Challenge:
• Bring together data streams from millions of
cargo equipment to enable predictive
maintenance
Solution:
• Sensor Data Analytics Framework based on
Cloudera and TCS to collect, store and
analyze data collected from port equipment
& machinery
• Improve utilization, reduce unplanned
equipment downtime
Smart Ports & Cargo Handling
DATA-DRIVEN
PROCESS
CASE STUDY
DATA-DRIVEN
PRODUCTS
TRAVEL & TRANSPORTATION
» INTERNET OF THINGS
» PREDICTIVE MAINTENANCE
» ADVANCED ANALYTICS
Leading Cargo Handling
Providers in Europe

39. 39© Cloudera, Inc. All rights reserved.
MINING & HEAVY MACHINERY
» ASSET OPTIMIZATION
» PREDICTIVE ANALYTICS
» INDUSTRIAL IOT
IoT enabled Asset Optimization
CASE STUDY
DATA-DRIVEN
PROCESS
DATA-DRIVEN
PRODUCTS
Optimize equipment performance and
costs using real-time IoT analytics
• Connected machinery includes some
of the largest mobile mining equipment
used in surface and underground
mining
• Data growth anticipated to reach 30
TB per month
• Cloudera on Azure to easily analyze
data from connected machines and
third party sources
• Doubled the utilization of a longwall
system for one of their Clients

40. 40© Cloudera, Inc. All rights reserved.
To Learn More…
https://www.cloudera.com/solutions/iot.html Cloudera Booth # 225

41. 41© Cloudera, Inc. All rights reserved.
Thank you
Questions?

42. 42© Cloudera, Inc. All rights reserved.
A Data Management Platform for IoT
Handle real-time
data ingest from
diverse sources
Fundamentally
Secure
Data
Streams
Machine Learning
Capabilities
Diverse
Analytical
Options
Enterprise Data Sources
Scale easily &
Cost effectively
Batch or Real- time
Data Streams
A comprehensive data management platform to drive business insights from IoT data
Data Sources
Data Storage &
Processing
Serving, Analytics &
Machine Learning
Data Ingest
Connected
Machines/ Data
Sources
Cloudera Enterprise Data Hub

43. 43© Cloudera, Inc. All rights reserved.
Cloudera Enterprise – Data Management & Analytics for IoT
BI Solutions Real-Time AppsSearch SQL
Analytics
Machine
Learning
Deployment
Flexibility
Spark Streaming
Leadership in Spark
Integrated with EDH
Flexible Storage
Store any and all Data.
Kudu – Real-Time
Analytics on Streaming
Data
Real-Time Data
Processing
Data Security
Four pillars of security: Perimeter,
Access, Visibility, and Data
+ Record Service
Streaming Ingest
Kafka & Flume - Real-Time
Data Ingest for streaming,
high volume data
Sensor/ IoT Data Internal Systems External Sources
Data Science
Cloudera Data Science
Workbench -
Collaborative hub for
enterprise data science
Manage Multiple Clusters – On
Premise or Cloud environment
- On Premise or Hybrid Cloud
OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Encrypt and KeyTrustee
Optimizer
BATCH
Sqoop
REAL-TIME
Kafka, Flume
PROCESS, ANALYZE, SERVE
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
Sentry, RecordService
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
SDK
Partners
Data Science
Workbench

44. 44© Cloudera, Inc. All rights reserved.
Cloudera in the Cloud - Hybrid Cloud Deployments
Flexible Deployments
• Multi-cloud: AWS, Azure, GCP
• Fast cluster deployments
• Scaling of CDH clusters
• Spot instance support
Easy Administration
• Dynamic cluster lifecycle management
• Single pane of glass: multi-cluster view
Enterprise-grade
• Integration across Cloudera Enterprise
• Management of CDH deployments at scale
Cloudera Director