AWS re:Invent 2017

The backend for the Snapchat Stories feature includes Snapchat's largest storage write workload. Learn how we rebuilt this workload for Amazon DynamoDB and executed the migration. Safely moving such a critical and high-scale piece of the Stories infrastructure to a new system, right before yearly peak usage, led to interesting challenges. In this session, we cover data model changes to leverage DynamoDB strengths and improve both performance and cost. We also cover challenges and risks in making remote calls across cloud providers, dealing with issues of scale, forecasting capacity requirements, and how to mitigate the risks of taking an unproven system through the dramatic traffic spikes that occur on New Year's Eve.

Sales on Prime Day 2017 surpassed Black Friday and Cyber Monday, making it the biggest day ever in Amazon history. An event of this scale requires infrastructure that can easily scale to match the surge in traffic. In this session, learn how AWS and Amazon DynamoDB powered Prime Day 2017. DynamoDB requests from Amazon Alexa, the Amazon.com sites, and the Amazon fulfillment centers peaked at 12.9 million per second, a total of 3.34 trillion requests. Learn how the extreme scale, consistent performance, and high availability of DynamoDB let Amazon.com meet the needs of Prime Day without breaking a sweat.

Database capacity planning is critical to running your business, but it's also hard. In this session we'll compare how scaling is usually performed for relational databases and NoSQL databases. We'll look behind the scenes at how DynamoDB shards your data across multiple partitions and servers. Finally, we'll talk about some of the recent enhancements to DynamoDB that make scaling even simpler, particularly a new feature called adaptive throughput that eliminates much of the throttling issues that you may have experienced.

Are you considering a massive data migration? Do you worry about downtime during a migration? Dr. JunYoung Kwak, Tinder's Lead Engineering Manager, will share his insights on how Tinder successfully migrated critical user data to DynamoDB with zero downtime. Join us to learn how Tinder leverages DynamoDB performance and scalability to meet the needs of their growing global user base.

Sprinklr delivers a complete social media management system for the enterprise. It also helps the world's largest brands do marketing, advertising, care, sales, research, and commerce on Facebook, Twitter, LinkedIn, and 21 other channels on a global level. This is all done on a single integrated platform. In this session, you learn about Sprinklr's journey to the cloud and discover how to optimize your NoSQL database on AWS for cost, efficiency, and scale. We also do dive deep into best practices and architectural considerations for designing and managing NoSQL databases, such as Apache Cassandra, MongoDB, Apache CouchDB, and Aerospike on Amazon EC2 and Amazon EBS. We share best practices for instance and volume selection, provide performance tuning hints, and describe cost optimization techniques throughout.

Airbnb has served over 200,000,000 customers across 191 countries and is one of the largest database consumers on AWS. They have heavily adopted MySQL and have recently completed a migration to Amazon Aurora. In this session, Airbnb shares their story, including design considerations for operating at Airbnb scale, tips, tricks, and advice for others startups, and thoughts on why they decided to run on Aurora.

Learn how Verizon is adopting the Amazon Aurora PostgreSQL-compatible edition for their mission-critical applications. Verizon has a history of adopting best of breed database technologies as they continue to serve their 140M+ customers. As Verizon moves its enterprise applications to the cloud, database performance and reliability are the key considerations. With heavy dependence on commercial databases, learn how a large enterprise like Verizon evaluated performance, reliability and operational characteristics of Amazon Aurora, and was able to create internal momentum behind adoption of open source technologies by showcasing early wins. This session also highlights best practices for using Amazon Aurora and the newly-announced RDS Performance Insights.

The IARPA Machine Intelligence from Cortical Networks (MICrONS) program is a research endeavor created to improve neurally-plausible machine-learning algorithms by understanding data representations and learning rules used by the brain through structurally and functionally interrogating a cubic millimeter of mammalian neocortex. This effort requires efficiently storing, visualizing, and processing petabytes of neuroimaging data. The Johns Hopkins University Applied Physics Laboratory (APL) has developed an open-source, highly available service to manage these data, called the Boss. The Boss uses AWS to provide a cloud-native spatial database with an innovative storage hierarchy and auto-scaling capability to balance cost and performance. This system extensively uses serverless components to meet both scalability and cost requirements. In this session, we provide an overview of the Boss, and we focus on how the APL used Amazon DynamoDB, AWS Lambda, and AWS Step Functions for several high-throughput components of the system. We discuss both the challenges and successes with serverless technologies.

Amazon Aurora is a fully-managed relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. The initial launch of Amazon Aurora delivered these benefits for MySQL. We have now added PostgreSQL compatibility to Amazon Aurora. In this session, Amazon Aurora experts discuss best practices to maximize the benefits of the Amazon Aurora PostgreSQL-compatible edition in your environment.

Today, small software teams have the ability to disrupt big markets as more and more businesses start to deliver their products as-a-service. The ability for teams to respond to customers and innovate quickly is their key differentiator. In this session, we will cover how you can begin your DevOps journey by sharing best practices used by the "two pizza" engineering teams at Amazon. We will showcase how you can accelerate developer productivity by implementing continuous Integration and delivery workflows using AWS Developer tools including AWS CodeStar, AWS CodeCommit, AWS CodeBuild, AWS CodePipeline and AWS CodeDeploy. Finally, we will demonstrate how to build an end-to-end CICD pipeline with CodeStar in minutes.