Learn how leading organizations are using analytics and machine to derive value from IoT data.

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IoT & Smart Cities
How Leading OrganizationsAre UsingAnalytics & Machine
Learning to Derive Value from IoT

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2 PB of data/car/ year 1 – 2 TB of data / day 1 – 5 TB of data / day

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IoT Data Characteristics
- Why this is a Big Data andAnalytics Problem?
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

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Cloudera Enterprise – The Data Mgmt. Platform for IoT
Connected
Devices/ IoT Data
Sources
Internal Systems External 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

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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
Ability to drive machine learning on
data with unified security,
governance & management
capabilities across clusters, clouds
and hybrid environments
Kudu: Real-Time Analytics Machine Learning with SDX Data Science Workbench
Collaborative hub for enterprise
data science and an integrated
development environment for
running Python, R, & Scala with
support for Spark

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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

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PATTERN
RECOGNITIO
N
ANOMALY
DETECTIO
N
PREDICTION
SELF-SERVICE
INTELLIGENCE
SECURE
REPORTING
REAL-TIME
ANALYTICS
MACHINE LEARNING ANALYTICS
Enterprise-proven machine learning and analytics
800+CUSTOMERS RUN
ON
750+CUSTOMERS RUN
ON

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Supports multiple languages — TensorFlow, R, Python
Direct, secure access to production data
with Impala & Spark
Collaborative and reproducible data science
Accelerating data science and ML from
exploration to production
Continued machine
learning innovation

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Powering a variety of IoT use cases…
Connected Vehicles
Usage Based Insurance
Industrial IoT
Predictive Maintenance
Smart Cities Smart Energy & Utilities
Aerospace IoT Healthcare IoT

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IOT - Key Customer Use Cases

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Using Predictive Maintenance to Improve
Performance and Reduce Fleet Downtime
• Real-time visibility of 375,000+ trucks in
order to improve uptime and vehicle
performance
• OnCommand Connection is collecting
telematics and geolocation data across
the fleet
• Centralizing data from 24 Telematics
service providers with varying frequency
and semantic definitions
• Reduced maintenance costs from $.12-
$.15 per mile to $.03 per mile
TRANSPORTATION
» PREDICTIVE MAINTENANCE
» IMPROVED SERVICE
» DATA DRIVEN PRODUCTS
IOT &
Connected
Products
CASE STUDY

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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.
• Optimize winter weather operations
Smart Cities
2016 Data Impact Award Winner
State of Kentucky Department
of Transportation
CASE STUDY

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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

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Thank you

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Backup Slides

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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

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#1 Telematics provider with 170 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

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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

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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

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Predictive Maintenance on Thousands
of Industrial Machinery in Real- Time
Challenge:
• Collect and analyze data from thousands
of diverse manufacturing systems in real-
time
Solution:
• iTrak application using Cloudera in the
Cloud to monitor the performance of
individual manufacturing systems in real-
time
• Predictive Maintenance - Proactively
identify & fix issues before they break
MANUFACTURING
» INDUSTRIAL IoT
» PREDICTIVE MAINTENANCE
» IMPROVED EFFICIENCIES
Industrial IoT – Predictive Maintenance
DATA-DRIVEN
PROCESS
CASE STUDY
DATA-DRIVEN
PRODUCTS

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AIRCRAFT & AVIATION
» PROCESS IMPROVEMENT
» PREDICTIVE ANALYTICS
» IOT SENSOR DATA
IoT enabled Aircraft Maintenance
CASE STUDY
DATA-DRIVEN
PROCESS
DATA-DRIVEN
PRODUCTS
Improving Flight Safety with Rapid,
Data-Driven Decision Support
• Uncover patterns in aircraft
performance and parts that can help
Sikorsky engineers improve flight
safety and optimize aircraft
operations
• Fleet monitoring and anomaly
detection
• Extend useful life of key components
• Helps prevent unscheduled
maintenance and better prioritize
repairs

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Enabling retailers better understand in-
store shopper behavior in real time
Challenge:
• Track each consumer’s path and journey in
the store with high accuracy
• Managing data from retail IoT sensors
Solution/ Impacts:
• Footfall Analytics: Real-time analysis into
how shoppers are browsing in stores
• Helps retailers optimize product placement
and staff management
• Increased sales by 9% in a major category
for one retailer
RETAIL IoT
» FOOTFALL ANALYTICS
» REAL TIME INSIGHTS
» CUSTOMER/ PRODUCT
ANALYTICS
IoT Enabled Retail Analytics
CASE STUDY
DATA-DRIVEN
PROCESS
DATA-DRIVEN
PRODUCTS

23. 23© 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

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IoT – The Data Mgmt. Value Chain
Data Sources Data Ingest & Storage Data Processing Machine Learning Analytics/ BI
• Diverse Data Sources
• Intermittent data streams
• Batch/ Streaming
• Diverse schema/ formats
• Real-Time Data Ingest
• Big-Data platform
• Unlimited Storage
• Low Cost/ TB
• In-memory processing
• Real-Time Processing
• Data enrichment
• Data contextualization
• Machine Learning libraries
• Unsupervised M/L
• Supervised M/L
• Iterative data modeling
• Data Visualization
• Data Science
• SQL Analytics
• Search

25. 25© 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
OptionsCombine Data from Diff
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
Enterprise Data Hub