The document details Deloitte's collaboration with Amtrak to implement serverless computing solutions, illustrating a shift from traditional server-based architectures to serverless models. It discusses the benefits, challenges, and technological framework of serverless architecture, including improved processing speed and reduced costs. Key recommendations emphasize starting small, integrating existing tools, and understanding AWS limitations.

1. LeapfrogintoServerless
A Deloitte-AmtrakCaseStudy
S E R V E R L E S S C O N F 2 0 1 7
@GaryArora
Cloud Solutions Architect
Deloitte

2. 1. Share our serverless journey with Amtrak
2. How can traditional enterprises leapfrog into serverless?
3. Collect some geek swag!
Objectives

3. Containersareso2016!*
*Conditions Apply :-)

4. 4 | Copyright © 2017 Deloitte Development LLC. All rights reserved.
Road to serverless solutions
Paradigm shift in computing evolution
Focus on business logic
Stackabstraction
Serverless
Unit of scale: Functions
• Deploy in milliseconds
• Live for seconds
Containerization
Unit of scale: Application
• Deploy in seconds
• Live for minutes/hours
Virtual Machines
Unit of scale: Machine
• Deploy in minutes
• Live for week
Physical Machines
Unit of scale: Physical servers
• Deploy in months
• Live for years
Server based

5. ModernizingLegacyApplications
withServerless

6. Carries more riders than all
airlines combined!
DC Boston
85,700 Passengers/Day
30 million annual riders
Serves 500 destinations over
21K miles of routes
Top Speed: 150 mph
$3.2 Billion in Revenue
71%
Online

7. Challenges
• Unpredictable and slow
response time: Ranging from
few seconds – 1 hour
• Analytics & BI dashboard
refreshed data every 24 hours –
causing reports to be stale by 1
day
• Existing systems are expensive
to support and extend with new
features
• No single source of truth
• High level of data redundancy
• Lack of integration &
consistency
• Non standardized data storage
and data access protocols

8. Solution Approach
Serverless
Integrated within AWS ecosystem
Widest prod implementation & support
Native mobile support & caching
1
2
Relational DB: High familiarity = Fastest GTM
NoSQL: Flexible schema = future proof
Select DB Design
Select DB
Datastore centered

9. There’s a lot more than Lambda in
AWS Serverless stack
API Gateway:
Builds, deploys, and
manages micro
services.
Lambda
Runs code in response to events
Integratedwithother
AWScomponents
Dynamo DB
Managed NoSQL
Database
Redshift
Managed, petabyte-
scale data
warehouse
SNS
Managed push
notification for
multiple platforms
Kinesis
Real-time data
streaming and
analytics
S3
Durable, highly-
scalable cloud
storage

10. Solution Architecture
Redshift
ServiceTickets
Ticket Process
QUERY
Target
Systems
Automated
Notifications
Operations
Trade
Partners
AWS Cloud Native ServicesSource
Systems
Tickets
Booking
Tickets
Migration
Glacier: Archive
Ref Data
JBoss
Booking
Tickets
S3: Overflow
>400KB
Booking Process
Data Pipeline: Archive
Segments
1 HOURETL
ETL
Booking
Data Pipeline: Reporting
S3: Flatfile
S3: Consumer Archive
DynamoDB
S3: Reporting
Ticket Service Process
20K/min
7 DAYS
XML
Data
Integration
Enterprise
Services
Lambda Kinesis Lambda
Lambda Kinesis Lambda
Lambda Kinesis Lambda
CloudWatch
Monitoring,
Auditing, and
Logging Audit Log Lambda: Notification SNSAppDynamics
S3: History Archive
1.2K/min
20K/min
API
Enterprise
API
API
Enterprise
API
Reports &
Dashboards

11. TL;DR?

12. Simplified View
Event driven and Serverless Architecture
Lambda Kinesis LambdaNoSQL DB
Data Transformation
Lambda Kinesis Lambda
Data Transformation
Downstream
Consumers
Reporting &
Analytics
Data Warehouse
NoSQL DBMainframe
Archive
RESTful JSON
Microservices

13. Near-real-time reporting and
advanced analytics

14. Value Delivered
• Processing 1 million
transactions/day with a peak
load of 2K transactions/minute
• near real-time reports and
dashboards
• Laid out the groundwork for
decommissioning legacy
systems
• Low cost maintenance &
operations: No servers to
maintain, load-balance, or scale
• Single source of truth & entry
via JSON restful services
• NoSQL schema: Future proof
• Improved data accuracy by
supporting edge cases that were
previously missed
Developed and released in six months!

16. Serverless meets it’s big claims!
Lower infrastructure cost
Lower maintenance cost
Pay Per Use
Faster deployment
Faster performance
Continuous Scaling
Highly available
Increased speed Reduced costs

17. Infrastructure Model
Prod
24x7
Test
24x7
Dev
24x7
Prod
18 hours/day
Test
2 hours/day
Dev
8 hours/day
On-Prem Stack Serverless Stack
8 Load Balanced Instances
2 Load Balanced Instances
1 Instance
Production Capacity
Production Capacity
Production Capacity

18. BUT
Serverless is NOT one-size-fits-all

19. Serverless Use cases
• Long-running or complex computational
tasks
• Data migration from relational to NoSQL
• Applications requiring significant disc space
or RAM
• Applications requiring SSH server access
• Web applications
• Mobile backend
• IoT backend
• Real-time analytics and data processing
• Etc.
Most Suited Least Suited
J

20. Key Takeaways
• Serverless = Technology + Mindset shift
• Prototypes Prototypes Prototypes!
• Start small
• Reuse & integrate with existing tools as much as
possible
• Don’t forget DevOps!
• Understand AWS limits
• DynamoDB: 400 KB per item
• 5 GSI and 5 LSI. Batch processing. No nested
indexes.
• Lambda: 128 KB input per event. Cold starts.
• Kinesis Shards & DynamoDB partition
• Exponential back off algorithm
• Make CloudWatch and X-Ray your best friend
• Understand how pricing works
• A larger RAM lambda can be cheaper than
lower if it finishes executing much quicker
• IAM roles & policy
• Data migration is a beast!
Approach Technology

21. So, is Serverless for me?
Should I buy a house? Should I rent a Room?My Core Objectives
Packaged or
à la carte
services
Find individual
suppliers or
DIY
OOB FeaturesOOB Features

22. @GaryArora