Accelerating Business Agility with Serverless Microservices

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Julian Wood Ben Smith
@julian_wood @benjamin_l_s
Senior Developer Advocates
AWS Serverless Applications
Accelerating Business Agility
with Serverless Microservices

A Modern Digital Business
Modern Digital
Business
Operational
Efficiency
Technical
Resiliency
Access to
Insights
Business Agility

Architecture evolution
Monolithic application
Does everything
Shared release pipeline
Rigid scaling
High impact of change
Hard to adopt new technologies
Microservices
Does one thing
Independent deployments
Independent scaling
Small impact of change
Choice of technology
When the impact of change is small, release velocity can increase

Development transformation at Amazon: 2001–2002
monolithic application
+ teams
2001
Lesson learned: decompose for agility
2002
microservices
+ 2 pizza teams
Full ownership & autonomy
You build it, you run it
DevOps – small, nimble teams
Focused innovation

Deployment at scale
1000s of
teams
Micro-
services
CI/CD Serverless
>60 million deployments a year*

Changes to Compute

Where do teams focus their efforts?
Physical
Infrastructure
Virtual
infrastructure
Service software
(orchestration, routing)
Application
packages (OS,
runtimes)
What you need
Business Logic

LEVELOFABSTRACTION
FOCUS ON BUSINESS LOGIC
VIRTUAL MACHINES
Hardware independence
Faster provisioning speed (minutes/hours)
Trade capex for opex
More scale
Elastic resources
Faster speed and agility
Reduced maintenance
Computing evolution – A paradigm shift
Physical Infrastructure
Virtual infrastructure
Service software
Application
packages (OS,
runtimes)
Business logic

LEVELOFABSTRACTION
CONTAINER SYSTEMS
Platform independence
Consistent runtime environment
Higher resource utilization
Easier and faster deployments
Isolation and sandboxing
Start speed (deploy in seconds)
Service software
Application
packages (OS,
runtimes)
Business logic

AWS Lambda
AWS Fargate
LEVELOFABSTRACTION
Continuous scaling
Fault tolerance built-in
Pay for value
Zero maintenance
SERVERLESS
Service software
Application
packages (OS,
runtimes)
Business logic

Serverless Operational Model
No infrastructure to
provision or manage
Automatically scales
by unit of consumption
Pay for value
billing model
Highly available
and durable

Event-driven architecture
Decouple state from code using
messaging
APIs to expose services
Scale without capacity planning
Serverless Programming Model

”
“
Financial Engines Cuts Costs by 94% Using
AWS Lambda & Serverless Computing
They replaced 50 IPO servers running in
21 different environments with four
Lambda endpoints to handle the same
amount of traffic. IPO requests trigger a
Lambda to run the IPO code. AWS
Lambda can run as many copies of the
function as needed in parallel,
dynamically allocating compute capacity
to match the rate of incoming requests.
• Saves 94% on hard costs
• Handles 10x normal traffic during
peak times
• Reduces operational costs
• Creates near-zero downtime
SolutionChallenge
Company: Financial Engines
Industry: Financial Regulatory Non-
Profit
Country: United States
Website: www.financialengines.com
Financial Engines is the largest
independent investment advisor in the
United States for assets under
management. They offer financial help
to more than nine million people and
manage more than one million
individual investment accounts worth
more than $160 billion.
”
“We project 94 percent savings in hard costs, or
about $110,000 annually. That’s not including the
cost savings in operational maintenance—security
patches and library upgrades—which AWS Lambda
takes care of automatically.
-Alex Yavorskiy, Chief Technology Officer, Financial Engines
About Financial Engines
Financial Engines needed the ability
to quickly recover from a potential
major disruption. They needed to
decrease the time their teams spent
on event monitoring and analysis
and server management.
Benefits

”
“
FINRA Doubles Cost Efficiency with AWS
Lambda
Data is ingested into Amazon S3 via
FTP and AWS Lambda performs the
validations. Amazon EC2 manages
data feeds coming into, and
notifications going out of, AWS
Lambda. Amazon SQS is used for input
/output messaging notifications while
Amazon VPC partitions the system into
separate test and production accounts.
• Delivered solution in three months
• Accelerated data validation by
700%
• Increased cost efficiency by a factor
of two
SolutionChallenge Benefits
Company: FINRA
Industry: Financial Regulatory Non-
Profit
Website: www.finra.org
The Financial Industry Regulatory
Authority (FINRA) is a not-for-profit
organization which protects investors
and ensures market integrity through
effective regulation of 3,800 broker-
dealers. They write and enforce rules,
examine firms for compliance, foster
market transparency, and educate
investors.
”
“Using AWS Lambda, we’ve increased cost efficiency
by a factor of two. We only pay for what we use, and
we don’t have to manage on-premises server
infrastructure.”
- Tim Greisback, Senior Director of Technology, FINRA
About FINRA
FINRA validates the data of 50,000
broker-dealer OATS (Order Audit
Trail System) files and formats
them according to 200 rules, which
totals half a trillion validations
each day. FINRA needed a solution
that could scale with processing
demand, which can double or triple
in response to market conditions.

”
“
Thomson Reuters Achieves 200%
Productivity Increase with AWS Lambda
When news breaks, Thomson
Reuters’ traffic doubles or triples.
They needed a way to elastically
scale and were tasked with producing
a solution on a tight timeline—5
months. They also needed to encrypt
and protect information in transit
and at rest, while handling thousands
of events per second.
Thomson Reuters built a solution that
ingests events using Amazon Elastic
Load Balancer, encrypts events using
AWS Key Management Service (KMS),
and hands them off to a streaming
data pipeline using Amazon Kinesis
Streams, Amazon Kinesis Firehose,
and AWS Lambda. AWS Lambda then
auto-batches and delivers data to
Amazon S3 for storage.
• Achieved 200% productivity
increase
• Launched two months ahead of
schedule
• Allows for fast data analysis &
quick internal adoption
• Optimizes streaming data
processing costs
SolutionChallenge Benefits
Company: Thomson Reuters
Industry: Worldwide News
Website: www.thomsonreuters.com
Thomson Reuters is a leading source of
information, including one of the world’s
most trusted news organizations, for the
world’s businesses and professionals.
”
“Our initial goal was to accommodate 2,000 events per
second. Now, we can process up to 4,000, and within a
year we expect to increase that to more than 10,000
events per second.”
- Anders Fritz, Senior Manager, Product Innovation
About Thomson Reuters

Nasdaq CIO Sees Serverless Computing as a 2020
Tech Trend
The technology “is reducing capital costs and shifting the focus to
customers’ needs instead of setting up, configuring, patching and
maintaining servers in the data center,” Brad Peterson, Nasdaq Inc.’s CIO
“A better client experience for a lower price point is on offer for those
willing to understand the technology,” Nigel Faulkner, head of
technology at T. Rowe Price Group Inc.
https://www.wsj.com/articles/nasdaq-cio-sees-serverless-computing-as-a-2020-tech-trend-
11579213416?_lrsc=7e26e436-569f-49b6-92b6-304160490541

Systems Integrators specializing in serverless

Growing Partner Ecosystem

Build PCI and HIPAA compliant serverless
applications!
Serverless platform services that can be used in both:

Serverless and Functions

Event-driven compute
Functions as a service
Serverless FaaSServerless FaaS
Lambda

Common AWS Lambda use cases
Web Apps Backends Data
Processing
Chatbots Amazon Alexa IT Automation
• Static
websites
• Complex
web apps
• Packages
for Flask
and
Express
• Apps &
services
• Mobile
• IoT
• Real time
• MapReduce
• Batch
• Powering
chatbot
logic
• Powering
voice-
enabled
apps
• Alexa Skills
Kit
• Policy
engines
• Extending
AWS services
• Infrastructure
management

Fine-grained pricing
Buy compute time in 100ms
increments
Low request charge
No hourly, daily, or monthly
minimums
No per-device fees
Never pay for idle
Free Tier
1M requests and 400,000 GBs of compute.
Every month, every customer.

Serverless is more than compute
APPLICATION PRIMITIVES – COMPUTE AND DATASTORES
AWS
Lambda
AWS
Fargate
Amazon
API Gateway
Amazon
SNS
Amazon
MQ
Amazon
SQS
AWS
Step Functions
APPLICATION INTEGRATION
DEVELOPER TOOLS
SECURITY AND ADMINISTRATION
Amazon Aurora
Serverless
Amazon
S3
Amazon
DynamoDB
AWS
AppSync
AWS
IAM
Amazon
Cognito
Amazon
Inspector
Amazon
VPC
Amazon
GuardDuty
AWS
CloudFormation
AWS
Cloud9
AWS
CloudTrail
Amazon
CloudWatch
AWS
X-Ray
AWS
CodePipeline
AWS
Config
AWS
SSO
AWS
Shield
AWS
WAF
Amazon
Kinesis
AWS Serverless
Application
Repository
Amazon
EventBridge
APPLICATION PRIMITIVES – COMPUTE AND DATASTORES

Adoption Patterns

Organic AdoptionLAMBDAUSAGE
IT AUTOMATION
LOG PROCESSING/
CRON JOBS
DATA TRANSFORMATION MICROSERVICES
DEVELOPER DIRECTOR EXECUTIVE
Production
Mission Critical
Adoption Curve

Serverless Adoption PatternsLAMBDAUSAGE
I T A u t o m a t i o n D a t a T r a n s f o r m a t i o n B u s i n e s s C r i t i c a l
A p p l i c a t i o n
D E V E L O P E R D I R E C T O R E X E C U T I V E
CONSIDERATIONS
Rapid development
Time to market—agility
Organizational changes
Long-term commitment
Production
Mission Critical
Adoption Curve

Leap Frog AdoptionCAPACITYPROCESSESCOSTMODELS
OPE R ATIONAL PR OCE SSE S DE VE L OPME NT MODE L S
ON-PREMISESCLOUD
“LEGACY” ARCHITECTURES MODERN ARCHITECTURES
AWS
EC2
AWS
ECS
AWS
FARGATE
AWS
EKS
Containers
AWS
ECS
AWS
FARGATE
AWS
EKS

What changes
have to be made
in this new world?
Operational model
Architectural patterns
Software delivery
People

Changes to the operational model

Isn’t all of this very hard now that
we have lots of pieces to operate?

Comparison of shared operational responsibility
AWS Lambda
Serverless functions
AWS Fargate
Serverless containers
ECS/EKS
Container-management as a service
EC2
Infrastructure-as-a-Service
More opinionated
Less opinionated
AWS manages Customer manages
• Data source integrations
• Application-level runtime and updates
• Physical hardware, software, networking,
and facilities
• Provisioning
• Application code
• Security and network configuration
• Container orchestration, provisioning
• Cluster scaling
• Physical hardware, host OS/kernel,
networking, and facilities
• Service scaling
• Security config network config, management
tasks, application runtime updates
• Container orchestration control plane
• Physical hardware software,
• Worker hosts and service scaling
• Security config and updates, network config,
firewall, management tasks
• Physical hardware software,
• Instance scaling
• Security config and updates, network config,
management tasks
• Provisioning, managing scaling and
patching of servers

Changes to the architectural patterns

Architecture diagrams change from simple…
APPLICATION SERVERS
DATABASE SERVERS
WEB SERVERS

…to more complex
MICROSERVICE
API
API
MICROSERVICE
MICROSERVICE
EVENT
API
MICROSERVICE
EVENT
API
MICROSERVICE
APPLICATION
Mobile client
Client
IoT
PERSISTENCE PERSISTENCE

APIs are the front door of microservices

Event-driven architectures

Queues
Simple
Fully managed
Any volume
Pub/sub
Simple
Fully managed
Flexible
Amazon Simple
Queue Service
Amazon Simple
Notification
Service
Messaging
Synchronization
Rapid
Fully managed
Real-time
Amazon
EventBridge
Decouple state from code using messaging

And data streams
Ingest
Data streams
Data processing
Real-time
Data Store
Microservices
Performance at scale
Fast and Flexible
Amazon Kinesis
Data Streams
Amazon
Dynamo DB
Data Stream Capture

Cloud-native architectures are small
pieces, loosely joined

Changes to the delivery of software

developers
delivery pipelines
services
monitorreleasetestbuild
Monolith development lifecycle

developers services
delivery pipelines
Microservice development lifecycle

People

What Drives Our Priorities?
Excelling in service fundamentals
Availability, latency, security, scale and associated controls
Equipping developers and operators
Constantly improving tools for monitoring, troubleshooting, and auditing
Adding efficiency to application development patterns
New patterns through events, workflows, functions, and APIs

re:Invent
2019
Launches
Express Step Functions
Faster, Cheaper Step Functions
Orchestration
Lambda Enhanced Controls
Async Controls, Enhanced Streaming
Controls
Lambda Destinations
Send execution results to a Destination
Lambda Provisioned Concurrency
Managed Prewarming
API Gateway HTTP APIs
Faster, Cheaper APIs
RDS Proxy
Managed Database Connection Pools

Customers
Build better
products
Innovate
more often
Release
features
faster
Focus on
business
logic
Decouple
software
systems
Our goal is to automate and abstract away as much as is possible so
customers can focus on building applications for their business
We are witnessing a paradigm shift

Andy Jassy, CEO, AWS
“If Amazon.com were starting
today, it would go serverless.”

aws.amazon.com/serverless

Feedback appreciated 
https://rebrand.ly/lm-
serverless

Thank you!

Accelerating Business Agility with Serverless Microservices

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