Leapfrog into Serverless - a Deloitte-Amtrak Case Study | Serverless Conference 2017

This talk was delivered at the Serverless Conference in New York City in 2017. Deloitte and Amtrak built a Serverless Cloud-Native solution on AWS for real-time operational datastore and near real-time reporting data mart that modernized Amtrak's legacy systems & applications. With Serverless solutions, we are able leapfrog over several rungs of computing evolution.

Gary Arora is a Cloud Solutions Architect at Deloitte Consulting, specializing on Azure & AWS.

Transcript

1. Leapfrog into Serverless A Deloitte-Amtrak Case Study 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. Containers are so 2016! * *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 Stack abstraction 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. Modernizing Legacy Applications with Serverless
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 Integrated with other AWS components 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 Service Tickets Ticket Process QUERY Target Systems Automated Notifications Operations Trade Partners AWS Cloud Native Services Source Systems Tickets Booking Tickets Migration Glacier: Archive Ref Data JBoss Booking Tickets S3: Overflow > 400 KB Booking Process Data Pipeline: Archive Segments 1 HOUR ETL 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 SNS AppDynamics 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 Lambda NoSQL DB Data Transformation Lambda Kinesis Lambda Data Transformation Downstream Consumers Reporting & Analytics Data Warehouse NoSQL DB Mainframe 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 Features OOB Features
22. @GaryArora