This document summarizes Wyng's user-generated content marketing platform and serverless architecture. Wyng is a digital marketing company headquartered in New York City that builds campaign platforms for brands. Their platform ingests 500k user-generated social media posts per day from hashtags and mentions, filters and searches the content, and analyzes marketing trends. Wyng implemented a serverless architecture using AWS Lambda, Kinesis, S3, Elasticsearch, and other services to scale elastically and reduce costs by 50%. The summary discusses lessons learned around security, availability, and technical limitations of the serverless approach.

1. Campaign Debrief
User-Generated Content Stream
Built on Serverless
May 17, 2018
Tian Chu, Director of Engineering

2. About us
Wyng builds technology that powers compelling digital campaigns and
promotions for agencies and brands. Our culture is rooted in technology and
marketing, spanning diverse disciplines and decades of experience across mar-
tech, ad-tech, CX, UX, data, and core mobile and web technologies.
In 2011, Wyng powered the first ever hashtag campaign in connection with a
Super Bowl ad, and continues to evolve its platform to align with shifts in
consumer behavior. We believe great products are defined by intelligent
architecture and a passion for innovation.
Wyng is headquartered in the NoMad section of New York City.
2
• Wyng is a digital marketing campaign platform
• Enables marketers create online Trivia Quiz, Photo
Contest, Sweepstakes by drag & drop
• Founded in 2009
• Headquarters in New York City
• Customers: Nestlé, Dove, L'Oréal, Audible,
TripAdvisor and agencies, such as Ogilvy
• Big believer in serverless architecture

3. User-Generated Content (UGC)
3
of people trust images taken from
“people like them” over brand
created images
70%
61%
of people would be more likely to
purchase through an advertisement
containing user-generated content

4. Ingest
4
• Ingest user generated content from
Facebook, Twitter, and Instagram by
#hashtag and @mention
• ~ 500K / day on average
• > 100K / hour at peak
• Content from different sources need
to be transformed into ONE format

5. Curate
5
• Content appear in near real-time
• Filter and search over thousands of
UGCs with response time < 1 sec
• Create collections of content
• Reject inappropriate content
• Reach out for digital rights

6. Use
6
• Build influence on
social networks
• Engage & Inspire
• Drive businesses

7. Analyze (Kibana)
7
• Visualizes content streams
• Run analytics on marketing trends
on social networks
• Generate weekly marketing report
• Monitor for abnormal activity of
content stream ingestion

8. Serverless Architecture
8
Elasticsearch
Service
LambdaAPI
Gateway
ECS
Fargate
(Containers)
Webhook
PowerTrack API
Kibana
Content API
Lambda
Transform
Kinesis
Stream
Buffer
S3
Persist
Glacier
Archive
Lambda
Index
Reception
Reception
DynamoDB
Metadata

9. Reception and Buffer
9
LambdaAPI
Gateway
ECS
Fargate
(Containers)
Webhook
PowerTrack API
Kinesis
Stream
Buffer
Reception
Reception
Requirements Solution
Incoming data volume is
unpredictable
Lambda scales to meet
demand
Cannot afford missing any
data
Lambda is highly available
Cannot afford lose any
data
Kinesis persists data for 7
days
PowerTrack needs long-
lived connections, but no
much CPU
Docker container
provisioned with 0.25 vCPU
on ECS Fargate

10. Transform, Persist and Search
10
Elasticsearch
Service
Kibana
Content API
Lambda
Transform
S3
Persist
Glacier
Archive
Lambda
Index
DynamoDB
Metadata
DynamoDB makes Lambda stateful
S3 and Glacier provides affordable persistence of
huge amount of data
Elasticsearch makes data ready for analytics and
search

11. Pros & Cons
11
Pros Cons
No server management Locked in to AWS J
Easier and quicker to scale Technical Limitations
High availability out-of-box
Hard to mimic AWS infrastructure on Dev
build
Cost effective (>50% reduction for us) Enterprise readiness?

12. Lessons Learned - Lambda
12
General Security
Use a framework Run everything in VPC
Familiarize yourself with technical limitations Use AWS Secrets Manager
Load test everything Use WAF to protect your endpoints
Dedupe is a must

13. Lessons Learned - Elasticsearch
13
Availability Security Index
Lots of memory!
Run Elasticsearch as non-
privileged user
Define a mapping
Use dedicated masters
Do NOT directly expose
Elasticsearch to users
Define _id for document
Sanity check queries
Avoid having too many
dynamic fields
Use time-based indices for
“rollover”

14. Wish List
14
Elasticsearch Service Lambda & Kinesis Stream
Auto scaling Lambda max duration > 5 mins
Continuous backup and point-in-time restore Kinesis max retention > 7 days
Support more custom plugins
In-place upgrade

15. Thank you!
tian@wyng.com (we are hiring!)