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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

3.
Who Am I?
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Drew Johnson
Vice 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

4.
Agenda
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• 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
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Volume of IOT data by 2018
90
percent
400
zettabytes
30
billion
IOT data in the cloud by 2020
Connected IOT devices by 2020

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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 Anatomy
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Requirements
• 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

8.
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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10.
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Aeris Technology Map
Web Services
MQTT
Data Storage
Indexing
Analytics
Location Geofence
and Search
Queuing
Netty
Tomcat
Netty
Aeris
Cassandra
Aeris
Aeris
RabbitMQ
HTTP Push
Aeris

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Key Database Requirements
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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
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• 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
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• 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

15.
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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 system
anomalies
OEM Case Study

16.
Moving Forward
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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)

17.
Q/A
Input questions at any time
using the online interface

18.
Thank You!
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