Plista, a Germany-based advertising solution provider, discusses how they use a cloud-native architecture, container-first approach to speed up development, increase agility, reduce latency, localize storage, and foster innovation and ownership in their organization. They demonstrate how a cloud blueprint is used to easily roll out their services to new global markets. With this architecture, Plista processes 1.7 billion requests per day, across four continents. They also discuss how they're adapting for GDPR compliance and redesigning parts of their platform to leverage new AWS services.

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Using Cloud Native for AdTech
Services on AWS to Grow Globally
Torben Brodt
Global Director Data
Engineering
plista GmbH
A D T 2 0 5
Philipp Hoffmann
Chapter Lead Data
Core
plista GmbH
Karl Bunch
Worldwide Technical Leader
Digital Advertising
Amazon Web Services

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Agenda
AdTech
plista’s 3 attempts to grow
Deep dives on Agility, Big Data and Scale

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AdTech
two tiers
Backend
smart
decisions
Serving (<=100ms)

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AdTech
Australia
2014
China
2016
APAC
2016
Germany
2008
North
America
2018

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AdTech
28
Markets
16
GB/hour
20,000
Recommendations/
sec

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1. Growing Australia
OPTIONS
a) Build as autonomous region
b) Only do serving from AU
DECISION DRIVERS
- double infrastructure
- double costs
- double manpower
- unclear growth
Germany
2008
Australia
2014

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1. Growing Australia
Serving from Australia +
Data processing from Germany*
*In DE we run on-prem

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Australia
1. Growing Australia
Guess what we learned!
Germany

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Australia
1. Growing Australia
CONCLUSION
Business was growing rapidly.
Tech fears did not come true.
We are GOOD!
WHAT WE LEARNED
- it’s extra operational complexity
- a lot of data transfer across regions
(costs + delays + problems)
- at this stage, still quite expensive
Germany

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We had similar uncertainties
For less data transfer problems, we
split Master Data into regions
For reasons of costs and complexity
we still wanted to re-use the Big
Data cluster in Germany.
China
2016
APAC
2016
2. Growing APAC & China

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2. Growing APAC & China
Germany: Big Data
Singapore: Master Data + limited backend processing
CN: Serving

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China
APAC
2. Growing APAC & China
Guess what we learned!
Germany

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China
APAC
2. Growing APAC & China
CONCLUSION
- we added even more complexity
- good cost
WHAT WE LEARNED
- underestimated Big China firewall
- still many problems with data transfer across regions
Germany

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3. Growing America
No more fears, age of containers
This time we wanted to make it
right!
- Cloud Native + Autonomous
- Terraform Blueprint
- Aurora
- ElasticCache
- Datalake
- etc
North
America
2018

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3. Growing America
Ohio: Serving + Master Data + Data Lake + Backend
Processing

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3. Growing America
Guess what we learned!
North
America

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3. Growing America
CONCLUSION
- we are confident in cloud
- this now serves as blueprint
WHAT WE LEARNED
- faster in development / rapid
prototyping
- focus on business + innovation
North
America

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22. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Karl Bunch
Worldwide Technical
Leader Digital Advertising
Amazon Web Services
Torben Brodt
Global Director Data
Engineering
plista GmbH
Philipp Hoffmann
Chapter Lead Data Core
plista GmbH

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PoC
● Learn AWS
● Build a pilot
project
● Build support
within
organization
Data Migration
● Leverage
different
storage
options
● Create a
rollback plan
● Migrate
Application
Migration
● Forklift
migration
● Hybrid
migration
strategy
Optimization
● Utilization
● Monitoring
● Efficiency
● Performance
● Re-
engineering
Assessment
● Assess
architecture
● Assess costs
● Assess
Security
Leverage the
Cloud
● Autoscaling
● Automation
● Elasticity
● High-availability
Cloud Migration Phases

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Terraform
Prerequisites
- Unified, cloud-agnostic configuration
- Avoid a vendor lock-in
- Open-source
Keys
- Single abstraction layer (no more
provider’s IDs in our configuration)
- Declarative configuration language
(don’t need to care about API calls order)
- Configuration could be used as inventory
source
- Open-source! :)

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Serverless Data Lake

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GDPR

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Spot Instances / Auto-Scaling

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Spot Instances / Auto-Scaling
~50%
cost
saving

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Going Cloud Native?
On-Premise AWS Comments
Caching Redis Redis/ElastiCache
No cross-region
replication for EC
Messaging Kafka Kafka
Integration with
existing stack
Resource Manager Mesos EMR
Spark works well on
both
Container Orch. Mesos ECS/(EKS)

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Going Cloud Native?
On-Premise AWS Comments
Offline Storage HDFS S3
Spark integrates with
both
SQL MySQL Aurora Compatible interface
Data Processing Spark Spark/EMR
Load Balancing DNS/haproxy DNS/ELB

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Data Processing (Single-Region)

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Data Processing (Multi-Region)

38. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Karl Bunch
Worldwide Technical
Leader Digital Advertising
Amazon Web Services
Torben Brodt
Global Director Data
Engineering
plista GmbH
Philipp Hoffmann
Chapter Lead Data Core
plista GmbH