AWS re:Invent 2014

The only way to accurately see how your website performs on heavy load, spiky usage, and with different web browsers and platforms is to do load and UI testing. This session explains how Amazon.com uses Amazon EC2 to do automated UI testing, at scale. We also cover using a variety of open source and commercial load and performance testing tools. These tools help you identify weak points in your web architecture, fix them, and ensure that your website can scale to the demand of your users. This session shows DevOps engineers, systems administrators, software developers, QA specialists, and front-end developers how to use tools and services like Amazon CloudWatch, Selenium, Siege, Bees with Machine Guns, and New Relic to load test their websites and applications running on AWS, and achieve desired levels of performance.

With Amazon Web Services it's possible to serve the needs of modern high performance advertising without breaking the bank. This session covers how AdRoll processes more than 60 billion requests per day in less than 100 milliseconds each using Amazon DynamoDB, Auto Scaling, and Elastic Load Balancing. This process generates more than 2 GB of data every single second, which will be processed and turned into useful models over the following hour. We discuss designing systems that can answer billions of latency-sensitive global requests every day and look into some tricks to pare down the costs.

For traditional data center applications, capacity is a fixed upfront cost. Thus, there is little incentive to stop using capacity once it's been allocated, and it has to be overprovisioned most of the time so there is enough capacity for peak loads. When traditional application and operating practices are used in cloud deployments, immediate benefits occur in speed of deployment, automation, and transparency of costs. The next step is a re-architecture of the application to be cloud-native, and significant operating cost reductions can help justify this development work. Cloud-native applications are dynamic and use ephemeral resources that customers are only charged for when the resources are in use. This talk will discuss best practices for cloud-native development, test, and production deployment architectures that turn off unused resources and take full advantage of optimizations such as reserved instances and consolidated billing.

Mining unstructured data for valuable information has historically been frustrating and difficult. This session will walk through practical examples of how multiple AWS services can be leveraged to provide extremely flexible, scalable, and available systems to successfully analyze massive amounts of data. Come learn how an application was adapted to leverage Elastic MapReduce and Amazon Kinesis to collect and analyze terabytes of web log data a day. Learn how Amazon Redshift can be used to clean up and visualize data and how AWS CloudFormation enables this analytical framework to be deployed in multiple regions while honoring privacy laws.

We have reached a tipping point in enterprise cloud adoption in higher education institutions. Many large research universities have now taken on various cloud projects, touching almost every aspect of their enterprise. One excellent example of this is Harvard University. Over the course of the past 18 months, Harvard University IT (HUIT) has been investigating, developing, and implementing an enterprise-scale cloud adoption program. This session presents HUIT's efforts to date, including approaches to vision, strategy, culture, education, staffing, and technology. The session also includes examples from other major universities. You'll receive practical advice about how to begin the adoption journey, and you'll learn about frameworks that can help you make decisions in the context of your own institutional environment.

Magento is a leading open source, eCommerce platform used by many global brands. However, architecting your Magento platform to grow with your business can sometimes be a challenge. This session walks through the steps needed to take an out-of-the-box, single-node Magento implementation and turn it into a highly available, elastic, and robust deployment. This includes an end-to-end caching strategy that provides an efficient front-end cache (including populated shopping carts) using Varnish on Amazon EC2 as well as offloading the Magento caches to separate infrastructure such as Amazon ElastiCache. We also look at strategies to manage the Magento Media library outside of the application instances, including EC2-based shared storage solutions and Amazon S3. At the data layer we look at Magento-specific Amazon RDSandndash;tuning strategies including configuring Magento to use read replicas for horizontal scalability. Finally, we look at proven techniques to manage your Magento implementation at scale, including tips on cache draining, appropriate cache separation, and utilizing AWS CloudFormation to manage your infrastructure and orchestrate predictable deployments.

AWS Sr. Vice President Andy Jassy delivers the Day 1 Keynote at AWS re:Invent 2014. Watch the latest news and innovations for Amazon Web Services and learn from AWS customers and partners who share how they're innovating with AWS.

Amazon.com CTO, Dr. Werner Vogels delivers the Day 2 Keynote at AWS re:Invent 2014. Learn about innovation on the AWS Cloud featuring exciting product announcements and customer stories.

Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability. Amazon Aurora joins MySQL, Oracle, Microsoft SQL Server, and PostgreSQL as the fifth database engine available to customers through Amazon RDS.

AWS Lambda is a compute service that runs your code in response to events and automatically manages the compute resources for you, making it easy to build applications that respond quickly to new information. AWS Lambda starts running your code within milliseconds of an event such as an image upload, in-app activity, website click, or output from a connected device. You can also use AWS Lambda to create new back-end services where compute resources are automatically triggered based on custom requests. With Amazon Lambda you pay only for the requests served and the compute time required to run your code, making it cost-effective and easy to scale automatically from a few requests per day to thousands per second.