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Aeris + Cassandra: An IOT Solution Helping Automakers Make the Connected Car a Reality

Drew Johnson, Vice President of Engineering, Aeris Communications

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Webinar Housekeeping

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Who Am I?

Drew JohnsonVice President of Engineering• More than 20 years of experience at both

large and small companies• Started career at Compaq Computer working

on the first generations of mobile computing devices

• Prior to Aeris, Director of Engineering at Austin mobile start-up SoloMio and VP of Engineering at mobile and messaging software company Openwave

• Holds a Masters degree in Artificial Intelligence from the University of Illinois and has more than 20 patents

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Agenda

• About Aeris Communications and IOT market trends

• The anatomy of an IOT PaaS

• Customer challenges in delivering IOT/M2M solutions

• The Aeris AerCloud PaaS offering

• Evaluating database technologies

• Case Study: How automotive OEMs leverage Aeris +

Cassandra

• Moving forward with Cassandra

• Q&A

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The Future is Here – IoT Trends

Volume of IOT data by 2018

90percent

400zettabytes

30billion

IOT data in the cloud by 2020

Connected IOT devices by 2020

About Aeris Communications

Enterprise/MNO Connectivity-as-a-

Service

Cloud-based IoT Applications and Analytics PaaS

Automotive Applications/

Services

Aeris IoT Platform

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The General IOT PaaS AnatomyRequirements

• Collect and store large amounts

of data (time-series, location, sensor

data)

• Reliable / secure

• Minimize data transfer

• Process in NRT for alerts

• Handle batch/deep analytics

• Share data in a secure manner

• Rapid prototype to production

scale

• Cost effective

Acceleration

Location

Speed Business Value

Traction

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Customer Challenges w/o PaaS

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• Customer developers focused on infrastructure rather than

differentiating features - suffering frequent downtimes

• Data intake volume

(Aeris: Grew from 1B transactions/year to almost 1B per day!)

• HIPAA / Data location compliance

• Need to derive more value from the data being captured

• Wild Wild West on device protocols – have to adapt easily

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How Aeris AerCloud Works

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Aeris Technology MapWeb Services

MQTT

Data Storage

Indexing

Analytics

Location Geofenceand Search

Queuing

Netty

Tomcat

Netty

Aeris

Cassandra

Aeris

Aeris

RabbitMQ

HTTP Push

Aeris

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Key Database Requirements

Strong real-time search and query capability

Support high reliability and security

Structured & unstructured real-time data

Flexible data model with affordable scalability

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Why Not Relational Databases

• Costly/Complex to scale – especially w/ HA

• Manual sharding at scale is a pain

• Inflexible data model – can’t handle IoT variety of data

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Why NoSQL and Apache Cassandra

• Purpose built for IOT & time-series data

• Optimized for storage & retrieval of time-series data

• Integrated analytics with Hadoop connector

• Fantastic write performance (2B MQTT/day @ 8 nodes)

• Simple to manage (No master/slave, shared storage, manual sharding)

• Supports 100% uptime through masterless architecture & multi-datacenter

replication

• Flexible schema allows for faster time-to-market

• Commodity storage/instance storage for linear & predictable scale

• No lock-in to a specific cloud provider

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Aeris has done especially well in Automotive

Aeris Automotive

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• Aeris handling 1M+ vehicles for an OEM• Recently identified key anomaly

due to software change• Same analytics engine could

identify other vehicle systemanomalies

OEM Case Study

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Moving Forward

Next up for Aeris – key customer areas:• Vehicle telematics – expanding use cases for both

make-money and save-money

• New verticals – avionics and fleet

Our 2 cents: Plan for Big Data and Data Portability from the start! (Cassandra/AWS > AWS alone)

Q/A

Input questions at any timeusing the online interface

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Thank You!