Date post: | 14-Aug-2015 |
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Technology |
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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!