Enabling the Connected Car Revolution 

1© Cloudera, Inc. All rights reserved.
Using Cloudera Enterprise to Derive Value
from IoT and Connected Cars
Enabling the Connected Car
Revolution
WEBINAR – IoT Series

Your Speakers for Today…
Vijay Raja
Sr. Solutions Marketing Manager
Cloudera
Dave Shuman
Industry Lead – Manufacturing & IoT
Cloudera
Jonathan Cooper-Ellis
Field Enablement Engineer
Cloudera

Connected Cars – The Market Opportunity
Connected Car Markets -
2020
250
Million
Connected Vehicles
$150
Billion
In Revenues
4x
Growth in
Revenues
Source: Gartner, Strategy&

Changing Consumer Appetite
37% of car customers in US,
Germany & China would switch to a
car made by a different manufacturer if
that were the only way to have full
access to their applications, data, and
media
Source: McKinsey

Connected Cars - Key Segments/ Use Cases
Mobility Management
Functions that enable drivers to reach a
destination quickly & effectively
Vehicle Management
Functions that reduce overall costs
and improve comfort
Well-Being
Functions that improve drivers
comfort and ability to drive
Autonomous driving
Operation of the vehicle without a
human driver at the controls
Safety
Functions that warn drivers of
external hazards
Entertainment
Functions involving entertainment of
driver and passengers
Source: Strategy&, 2015
Vehicle condition & performance, service reminders, remote mgmt.,
telematics etc.
Real-time traffic information displays, parking lot or garage assistance etc.
Collision avoidance, hazard warning signals and emergency call functions Self-parking cars, self driving mode, motorway assistance etc.
Fatigue detection & alerting and other forms of individual assistance Smartphone interfaces, streaming videos & access to social networks

Connected Car Use Cases - Examples
Predictive Maintenance
Customer 360
Usage Based
Insurance
Telematics & Fleet
Management
Public Safety
Advanced Vehicle
Diagnostics
Usage & Feature
Analytics
Maintenance
recommendations &
analytics based on
vehicle usage
Insurance coverage
based on specific
driving habits & risk
Targeted and personalized
offers for customers based
vehicle condition & driving
habits
Notify drivers on traffic
patterns, safety hazards,
accidents etc.
What features in the
vehicle are most
used, what are least?
Fleet performance and
management based on
vehicle usage
Provide a real-time view
of vehicle health &
performance

Polling Question - 1
Where is your organization in your Connected Car journey?
A. Not sure where to start
B. Currently exploring use cases
C. Implementing our first use case
D. Already deployed first use case
E. Multiple C-Car use cases in production
(Single Choice)

1 GB of data per sec
2 PB of data/car/ year

Data from Connected Cars
Significant Data volumes
• 25 GB per hour per car
• 130 TB per year per car
130TB
25GB
per hour
per car, per year
Diverse Data Types
• Data from ECUs
• Speed, RPM, Fuel Efficiency,
Temp, Pressure, Braking…
• Location Data
• Safety Data
• V2V and V2I
• Video
Data Variety
• Streaming (Real-Time)
• Batch
• Multiple Formats
Data Sources
• ECU (Electronic control
units)
• Vehicle Plug-ins
• Head units
• Cameras

Connected Car – A World of Hybrid Analytics
• Edge processing and analytics that
needs to be acted upon immediately
• Low latency req. - Hazard detection,
collision avoidance etc.
• Human response times
• V2V Communications
• Time series Analysis
• Near-Real time analytics
• Trend Analysis
• Machine Learning
• Context Enrichment
• Wear and tear monitoring
• Advanced Vehicle Diagnostics
• Location Analytics
• Driving Analysis
Cloud
Analytics
Edge
Analytics
Cloud
Analytics

Value is maximized when data is combined with other
sources
Value of sensor data is multiplied when you can
add context to it and correlate it with data from
other sources
Improvement in value that can be
unlocked by combining data from
multiple IoT applications and sources
SOURCE: McKinsey Global Institute analysis
Interoperability would significantly improve performance by
combining sensor data from different machines and systems to provide
decision makers with an integrated view of performance
40%

Connected Vehicles – Building a 360° View
14
What’s the average
speed?
What are their in app
purchases?
What kind of fuel is
being used?
How is the engine
performing?
What features are being
used the most/least?
When is the next
maintenance
required?
Where did they go on
their last trip?
Who do they know
that might buy our
vehicles?
When is the best time
to drive to the office?

FILESYSTEM RELATIONAL
Cloudera Enterprise – Making Hadoop Fast, Easy, and Secure
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
CLOUDERA ENTERPRISE

Unifying Diverse Data Sources to Drive Value…
Customer Data
(CRM)
Dealer Data
Parts & Warranties
Streaming Vehicle
Sensor Data
OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Optimizer
BATCH
Sqoop
REAL-TIME
Kafka, Flume
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
SDK
Partners
Geo-location/
Traffic Data
Manufacturing/
Suppliers
Other External
Data SourcesService &
Maintenance
Enterprise ERP
Data

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

Managing Data from Connected Vehicles
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

Vehicle Data Collection, Storage and Analysis
Data Transmission
Compressed Data (Batch /
Stream)
- Hadoop based Architecture
Store Enrich
Vehicle
Metadata
Sensor
Metadata
Process
ApplicationsBI Tools
MLLib
Learn /
Predict
Serve
Ingest
MQTT
CoAP

Cloudera Enterprise – Real Time Analytics for IoT
BI Solutions Real-Time AppsSearch EDWDiscover Machine
Learning
Deployment
Flexibility
Spark Streaming
Leadership in Spark
Integrated with EDH
Flexible Storage
Store any and all Data.
Kudu - Fast Analytics on
Fast 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
Centralized Mgmt.
Cloudera Manager for
centralized cluster
management
Manage Multiple Clusters – On
Premise or Cloud environment
- On Premise or Cloud
OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Optimizer
BATCH
Sqoop
REAL-TIME
Kafka, Flume
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
SDK
Partners

Connected Car – Case Studies

How can we consistently monitor
performance of our vehicles?

Enabling Leading Auto Manufacturer to
Effectively Capture & Analyze 30,000+
Signals from Sensors in Every Car
Challenge:
• Effectively capture and analyze 30,000+
signals & data points from sensors in
every car
Solution:
• Centralized data platform from Cloudera
for analytics of all of the sensor data
• Monitor individual component
performance and operational metrics
AUTOMOBILE
» CONNECTED CARS
» PREDICTIVE ANALYTICS
» REMOTE MONITORING
Connected Cars
CASE STUDY
DATA-DRIVEN
PROCESS
DATA-DRIVEN
PRODUCTS

How can we lower vehicle
downtime?

Using Predictive Maintenance to Improve
Performance and Reduce Fleet Downtime
• 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
• Real-time visibility of 250,000+ trucks in
order to improve uptime and vehicle
performance
TRANSPORTATION
» PREDICTIVE MAINTENANCE
» IMPROVED SERVICE
» DATA DRIVEN PRODUCTS
DATA-DRIVEN
PRODUCTS

How can we offer personalized insurance based on
individual driving habits?

Usage Based Insurance to reduce claims
by a large European insurance agency
Challenge:
• Gather, store and analyze sensor data
from millions of customers to analyze
driving habits & risks
Solution:
• Telemetry solution on Cloudera EDH -
increased policy renewal and customer
satisfaction rates
• Reduced the number of claims by 30%
resulting in savings in millions annually
INSURANCE
» CONNECTED VEHICLES
» USAGE BASED INSURANCE
» REDUCED COSTS
Usage-Based Insurance and Telematics
CASE STUDY
DATA-DRIVEN
PROCESS
DATA-DRIVEN
PRODUCTS

#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
TELEMATICS
» CONNECTED VEHICLES
» INSURANCE TELEMATICS
» PREDICTIVE ANALYTICS
Connected Car Telematics for Insurance
CASE STUDY
DATA-DRIVEN
PROCESS
DATA-DRIVEN
PRODUCTS

Powering a Variety of IoT Use Cases…
Connected Vehicles
Usage Based Insurance
Industrial IoT
Predictive Maintenance
Smart Ports Smart Healthcare
Connected Products Aerospace & Aviation

Connected Car Demo
Jonathan Cooper-Ellis, Field Sales Engineer

Connected Car – Demo Architecture
OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Optimizer
BATCH
Sqoop
REAL-TIME
Kafka, Flume
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
SDK
Partners
Cloudera Enterprise Data Hub
MQTT -
Kafka
Bridge
Connected Car
Simulator
Data Ingest &
Pipeline
Enterprise Data Hub BI & Visualization
Streaming Data:
• Time
• VIN
• Location
• Mileage
• Speed
• Acceleration
• Brakes applied?
• Turn signal on?
• Lane departed?
• Collision
detected?
• Hazard detected?
StreamSets Data
Collector

Connected Car – Demo Architecture
Cloudera Enterprise Data Hub
MQTT -
Kafka
Bridge
Connected Car
Simulator
Data Ingest &
Pipeline
Enterprise Data Hub BI & Visualization
Streaming Data:
• Time
• VIN
• Location
• Mileage
• Acceleration
• Speed
• Brakes applied?
• Turn signal on?
• Lane departed?
• Collision
detected?
• Hazard detected?
Data Storage Layer
Search
#2
#1
Pub-Sub Messaging
System
Real-Time
Processing Engine
StreamSets Data
Collector
Interactive SQL Engine

Key Use Cases Highlighted
Predictive
Maintenance
Public Services
Understand traffic patterns,
public safety hazards and
provide recommendations
accordingly
Usage Based
Insurance
Provide specific Insurance
coverage based on specific
driving habits & risk profiles
OEMs Insurance Public Services
Provide specific service
recommendations based on
vehicle usage and driving habits

Thank You

Managing Data in Motion & Data at Rest
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 Cars/ Sensor
Data
Structured Data Sources Security, Scalability & Easy Management
Deployment Flexibility:
Datacenter Cloud
Apache Spark
Stream & iterative processing, ML

Cloudera Enterprise – The Data & Analytics Platform for IoT
Internal Systems External Sources
Data Center
Cloud
Customer (CRM),
Manufacturing, Dealer Data
• Data Ingest
• Data Storage
• Data Processing
• Real-time Analytics
BI Solutions Real-Time AppsSearch EDWDiscove
r
Machine
Learning
OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Optimizer
BATCH
Sqoop
REAL-TIME
Kafka, Flume
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
SDK
Partners

• High throughput for big scans
(columnar storage and replication)
Goal: Within 2x of Parquet
• Low-latency for short accesses
(primary key indexes and quorum design)
Goal: 1ms read/write on SSD
• Database-like semantics
(initially single-row ACID)
• Relational data model
• SQL query
• “NoSQL” style scan/insert/update (Java client)
Enable Time Series Analytics with Kudu

Cloudera – IoT Technical Architecture
IoT Framework
Data Ingestion
(Kafka, Flume)
Data Transformation
and Enrichment
Data Processing &
Serving
Rule Mining, Pattern
Matching, Machine Learning
Distributed Data
Analytics
Data
Visualization
Data Storage
(Hbase, HDFS, Kudu)

The IoT Ecosystem & Hybrid Analytics
IoT Gateway
Data Center
Gateway
• Data Routing
• Edge-Processing
• Edge-Storage
Centralized Data Storage, Processing & Analytics
Centralized IoT Data Analytics
• Time Series Data, Trends
• Context Enrichment
• Deeper business insights
Distributed Data
Processing & Analytics
• Cloud & On-Premise
Cloud
Sensors/ Things
• Analytics at the edge
• For Immediate
response
IoT Analytics

① All Tesla cars are connected and provide feedback to the factory
② Tesla’s current fleet of cars’ records 1,500,000 miles of data EVERY DAY
③ All newer Teslas have auto-pilot hardware and are providing detailed data

Enabling the Connected Car Revolution

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (20)

Similar to Enabling the Connected Car Revolution

Similar to Enabling the Connected Car Revolution  (20)

More from Cloudera, Inc.

More from Cloudera, Inc. (20)

Recently uploaded

Recently uploaded (20)