How to Build Real-Time Interactive Applications: AWS Developer Workshop - Web Summit 2018

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Adrian Hornsby, Cloud Architecture Evangelist
@adhorn
How To Build Real-time Interactive
Applications

1879

4 September 1882
The Pearl Street Generating Station

The lightbulb was a product of networked
innovations, all linked together to create the
magic of electric light.

How would you reinvent the lightbulb today?

AWS IoT IoT
shadow
Amazon
Cognito
MQTT over WebSockets
AWS
LambdaAlexa
N
O
G
EN
IU
S
W
AS
IN
VO
LVED
Amazon
S3

What is the real power of the cloud?

Abstract complex problems into easy to use
services.

IoT
Real-Time Analytics
Machine Learning

Internet of Things

Registry: Identity for devices
Rules and Actions: Match patterns and take
actions
Shadows: Virtual representation of the
device in the cloud.
{Thing Name,
Sensor Temp,
, GetTemp(),
Output LED}
Rules Engine
Shadow
Registry
Amazon S3,
AWS Lambda,
Kinesis
DynamoDB
SNS
Elasticsearch
Machine Learning
Mobile App
AWS IoT: Key features

Secure by Default: Connect securely via
X509 Certs and
TLS v1.2 Client Mutual Auth
Multi-protocol Message Gateway:
MQTT, HTTP or WebSockets.
Elastic Pub Sub Broker: from 1 to 1-billion
long-lived connections with zero
provisioning
Subscribers
Publishers
AWS IoT: Key features

https://aws.amazon.com/solutions/case-studies/irobot/

https://aws.amazon.com/solutions/case-studies/british-gas/

Real-time streaming

Stream New Data in Seconds
Get actionable insights quickly
Streaming
Ingest video
& data as it’s
generated
Process data
on the fly
Real-time
analytics/ML,
alerts, actions

Data for Competitive Advantage

S3 → EMR→
Data collection and processing
DynamoDB
ElasticSearch

Clash Royal: Getting relevant value for the players

Real-time Analytics Demo
http://quad.adhorn.me

Real-time analytics
Amazon
Kinesis
Stream
Amazon
Kinesis
Analytics
Amazon
Cognito
Amazon
Kinesis
Stream
Amazon
DynamoDB
Amazon
Lambda
Amazon S3
JavaScript SDK

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data Input
Source JSON Data
• Once per second, using JavaScript SDK:
• Unique Cognito ID (anonymous user)
• OS
• Quadrant
• Data sent to Kinesis StreamAmazon
Kinesis
Stream
Amazon
Cognito
Amazon
S3
JavaScript
SDK
{
"recordTime": 1486505943.204,
"cognitoId": "us-east-1:3626e211-d2a3-447b-8231-e1f4e0486f44",
"os": "Android",
"quadrant": "A”,
…
}

Aggregation
CREATE OR REPLACE PUMP "OUTPUT_PUMP" AS
INSERT INTO "DESTINATION_SQL_STREAM"
SELECT STREAM
COUNT(dus.COGNITO_ID) AS UNIQUE_USER_COUNT,
COUNT((CASE WHEN dus.OS = 'Android' THEN COGNITO_ID ELSE null END)) AS ANDROID_COUNT,
COUNT((CASE WHEN dus.OS = 'iOS' THEN COGNITO_ID ELSE null END)) AS IOS_COUNT,
COUNT((CASE WHEN dus.OS = 'Windows Phone' THEN COGNITO_ID ELSE null END)) AS WINDOWS_PHONE_COUNT,
COUNT((CASE WHEN dus.OS = 'other' THEN COGNITO_ID ELSE null END)) AS OTHER_OS_COUNT,
COUNT((CASE WHEN dus.QUADRANT = 'A' THEN COGNITO_ID ELSE null END)) AS QUADRANT_A_COUNT,
COUNT((CASE WHEN dus.QUADRANT = 'B' THEN COGNITO_ID ELSE null END)) AS QUADRANT_B_COUNT,
COUNT((CASE WHEN dus.QUADRANT = 'C' THEN COGNITO_ID ELSE null END)) AS QUADRANT_C_COUNT,
COUNT((CASE WHEN dus.QUADRANT = 'D' THEN COGNITO_ID ELSE null END)) AS QUADRANT_D_COUNT,
ROWTIME
FROM "DISTINCT_USER_STREAM" dus
GROUP BY
FLOOR(dus.ROWTIME TO SECOND);

Persist aggregated data in DynamoDB
Kinesis Stream Lambda DynamoDB
event.Records.forEach((record) => {
const payload = new Buffer(record.kinesis.data, 'base64').toString('ascii');
var docClient = new AWS.DynamoDB.DocumentClient();
var table = "user-quadrant-data";
var data = JSON.parse(payload);
var params = {
TableName: table,
Item:{
"dataType": "quadrantRollup",
"windowtime": (new Date(data.WINDOW_TIME)).getTime(),
"userCount": data.UNIQUE_USER_COUNT,
"quadrantA": data.QUADRANT_A_COUNT,
"quadrantB": data.QUADRANT_B_COUNT, ...
}
};
docClient.put(params, function(err, data) { ...
Lambda event source mapping

Machine Learning

FRAMEWORKS AND INTERFACES
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
APPLICATION SERVICES
Amazon
Rekognition
Amazon Polly Amazon Lex
Machine Learning on AWS
Amazon
Rekognition Video
Amazon Transcribe
Amazon Comprehend
Amazon SageMaker AWS DeepLens Amazon EMR
Deep Learning AMI
Amazon Translate

Autonomous Driving Systems

FDA-approved
medical imaging
https://www.periscope.tv/AWSstartups/1vAGRgevBXRJl
https://www.youtube.com/watch?v=WE81dncwnIc

Marinus Analytics uses facial recognition to stop human
trafficking
“Now with Traffic Jam’s
FaceSearch, powered by
Amazon Rekognition,
investigators are able to
take effective action by
searching through millions
of records in seconds to
find victims.”
http://www.marinusanalytics.com/articles/2017/10/17/amazon-rekognition-helps-marinus-analytics-fight-human-trafficking

Demo – ML services (CLI) + Poliko
https://github.com/adhorn/poliko

Democratization of Technology

Go Build A
Smarter Future.
@adhorn
https://medium.com/@adhorn

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How to Build Real-Time Interactive Applications: AWS Developer Workshop - Web Summit 2018

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