AWS Anaheim Keynote 2018

Tags: aws awssummit

Keynote Speaker: Stephan Orban

  • General Manager, AWS

Speaker: Stephen Felisan, CIO, Edmunds

  • 20m car shoppers ech month
  • Pivoted in 1 year
  • Culture for speed
  • Data strategy
  • Embrace serverless

Data Strategy

  • S3 as data layer for everything
  • Sensitive data secured in S3


  • Banquet image processing system on Lambda
    • Pregenerate every image in every aspect ratio
    • Msg sent via HTTP with image info in body
    • Lambda does resizing
    • Over 1000 lambdas running in parallel generated over 1bn images in 3 days
    • Saved 1bn dollars
    • Now developing deep learning image classification system with more metadata
  • 13.9 avg page load time to 2.3 s
  • 2 years ahead of competition
  • Software agility means business agility

Compute Services (Stephan Orban)

Back to Stephan Orbal

  • From server to serverless
  • Focus on the code rather than infrastructure, focusing on business logic.

Virtual Machines

  • Generally monolithic components
  • Advantage: Fast upgrades on hardware
  • New T3s have 30% speed increase over T2s.
  • Higher end machines have large custom memory footprints
  • F1s have field programmable gates
  • Able to mix component elements, so M5 memory and F1 programmable gates


  • Container registry
  • Easy deployment of containers thanks to Elastic Container Service (ECS)
  • Multiple availability zones
  • Integrated into the rest of the AWS environment
  • Ability orchistrate tens of thousands of containers
  • Elastic Kubernetes Service (EKA) allows ease of kubernetes hosting
  • Fargate could be called "Serverless for containers". You are only billed for actual processing time


  • For when all you care about it the business logic, don't care about infrastructure
  • Scales to forever
  • Only pay for what you use
  • Really nice free tier 😉
  • Wired into tons of AWS services

Lambda Enterprise customer examples

Two companies who leverage Lambda to improve their systems and scale up

  • Finra
  • Autodesk

Speaker: Ethan Kaplan, CPO, Fender Digital

  • Yes, the 70 year old guitar company
  • From the beginning and they used iterative engineering to create products
  • Fender Digital has 40 employees in IT, 2000 overall
  • Stats
    • 45% of players are new players in the ast year
    • 90% abandon guitar in a year
    • 20% of people who continue will buy 10 guitars over their lifetime
    • Mission: Change the stats
  • Fender Play mobile was designed to change the stats. Free structured apps designed to help people learn.
  • Needed to be agile and scale up rapidly
  • They produce 30 lessons per day, how do they get that to an app
  • Use Video Processing and Lambda to process incoming videos.
  • Serverless had a huge cost benefit for them
  • Rely API GAteway / Serverless to read Apple/Google callbacks to put data into DynamoDB. Eventually data goes to Redshift for analytics. Lessons learned:
    • DynamoDB has a learning curve - knowing how to add data to make it easy to query is important.
    • Instrumentation could be better
    • Lambda cold start issues
    • Build/deploy times for a Go App started taking a huge amount of time. Compilation was a beast.
  • 40 services in use
  • 21x traffic this year but 20% cheaper by moving everything to serverless
  • Factories will be using IoT to control humidity and automation

Data & Analytics (Stephan Orban)

  • It can become expensive to run databases
    • Costs increased for cloud datastores
  • Running AI/ML servers is also a cost

Migration services

  • 87,000+ databases into the AWS ecosystem


  • MySQL or PostgreSQL
  • RDS in memory
  • Very fast

Aurora Serverless

  • MySQL or PostgreSQL
  • RDS in memory
  • Very fast
  • On-demand, auto-scaling database for applications with unpredictable or cyclical workload


  • About 80% of what we consider analytics is not analytics. Examples:
    • Data wrangling
    • Data transformations
    • Moving data to the right place (data is in silos)
  • Mission: Flip it so 20% of analytics is not analytics
    • Get data
    • Automate data wrangling
    • lifecycle management
    • Provide access to data

Using S3 as the analytics data storage

  • Unmatched reliability, accessbility, durability
  • Really good security
  • Object level controls
  • Versioning
  • Lifecycle policies including archiving for long term storage
  • Most ways to bring data in and out
  • Tons of partner integrations
  • Nigh-infinite formats because S3 is a file system!

S3 Select and Glacier Select

  • Run SQL expressions against s3 files
  • Only pay for the compute resources while executing your jobs

AWS Glue

  • For loading data into S3, Redshift, and other data lakes
  • Generates ETL scripts, loads transformations, and more
  • Dump your data and off you go!
  • Only pay for the compute resources while executing your jobs

Machine Learning (Stephan Orban)

  • Amazon retail uses machine learning to evaluate whethor or not an order is probably fraudulent.
  • 70% of things watched on Amazon Prime and Netflix are driven by recommendations made by machine learning
  • Machine learning had long been the domain of experts at Amazon
  • Took too long to implement ML for internal Amazon efforts, so they made it easier for staff.
  • 250% growth of ML on AWS this year
  • 80% of ML queries are estimated to run on AWS

AWS SageMaker

  • Lets you run all the platforms (Torch, etc)
  • Wrapper for Jupyter Notebook

Other tools

  • Polly: Text to speech
  • Rekognition/Rekognition Video
  • Transcribe
  • Translate

Copyright © 2018 Daniel and Audrey Roy Greenfeld.
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Last Updated: 8/31/2018, 11:57:19 PM