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Connecting 10000 cars to the AWS Cloud

Connecting 10000 cars to the AWS Cloud

Talk at AWS community Day Kochi

Ashwin Murali

October 12, 2019
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  1. $whoami • Old school Sys / Network Admin • <3

    Shell / UNIX • <3 Cloud and scaling tech companies • IoT is my new tinker toy • Own Mail and DNS servers
  2. Note: This works when copying entire slides from other presentations

    as long as the source presentation is also 16:9 India’s first and largest app-based self- drive car rental company.
  3. Note: This works when copying entire slides from other presentations

    as long as the source presentation is also 16:9 100% AWS
  4. Some more data • 40 Cities • 3mn users on

    the platform • > 4000 rides every day • 5lac km travelled daily • 10000 cars
  5. Some more data contd… • All cars are connected to

    platform • > 500mn IoT events ingested daily • > 1TB data lake for analysis and reporting
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    as long as the source presentation is also 16:9 This is just cars! We have scooters too ;-)
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    as long as the source presentation is also 16:9 WE BURNT OURSELVES, OUR FAMILIES, OUR FRIENDS, OUR LAPTOPS and then some!
  8. • ~ 70 % uptime • No NOC • Noisy

    neighbor problems • Hands tied on architectural, design, infra decisions • DevOps === Red eyed devils on the floor • Maintain / own Systems • Maintain / own Infra
  9. • Mandate to scale – Zap subscribe • 1 month

    to create migration plan • 2 days to create instances • 3 days to migrate data • 4 hours of downtime • 5 Star Performance
  10. Post the upgrade • Cloudfront – Website (1.2 secs to

    first paint) • Autoscaling API cluster (2sec to 150ms avg resp time) • EKS (~30% cost advantage) • Multi AZ RDS (No DBAs in office) • Read Replica Slaves (Daisy chained on demand for teams) • AWS Glue / Athena / Redshift (More data sources, more information) • 40X average scale / 200X peak load (Yaaaaay!!!) • Elasticache (Memcache / Redis at their performant best) • Kafka for IoT pipelines (Beast mode on steroids for distributed data processing)
  11. Business Intelligence • User behavior capturing • Quicker identification of

    UX drop off points • Demand Forecasting • Predictive Maintenance
  12. Data Analytics • Every data point we can think of

    captured • Cars produce 500mn data points per day • Customer Churn • Fraud detection • Driver Score
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    as long as the source presentation is also 16:9 And that’s how we connect 10000 cars to the AWS Cloud…
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    as long as the source presentation is also 16:9 Hold on! There’s more!
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    as long as the source presentation is also 16:9 How would YOU do it?
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    as long as the source presentation is also 16:9 How would you connect 1mn devices?
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    as long as the source presentation is also 16:9 Few Key Learnings
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    as long as the source presentation is also 16:9 AWS IoT
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    as long as the source presentation is also 16:9 Key Components
  20. Key Components • Applications • Device Management • Message management

    • Storage • Data Processing • Notifications • Analytics
  21. Key Components • Applications – AWS IoT SDK / Edge

    with Greengrass • Device Management – AWS IoT Hub / Device Shadows • Message management – AWS IoT Message Queue / AWS IoT Rules Engine • Storage – RDS / DynamoDB • Data Processing - Lambda • Notifications – SQS • Analytics – EMR / Kinesis / Redshift
  22. • Rock solid / proven ecosystem • Infinite Scale •

    Insanely small Time to Market • Programmability • Insights • Smooth learning curve • Existing support systems and community
  23. Note: This works when copying entire slides from other presentations

    as long as the source presentation is also 16:9 Thank You [email protected] +91 9003010231