Learn about architecting a highly available RDS MySQL implementation to support your high-performance applications and production workloads. We will also talk about best practices in the areas of security, storage, compute configurations, and management that will contribute to your success with Amazon RDS for MySQL. In addition, you will learn about how to effectively move data between Amazon RDS and on-premises instances.

Come learn how to turbocharge your application development with useful tips and tricks for Amazon RDS for SQL Server. We will cover practical and useful topics such as migrating your data to an Amazon RDS for SQL Server instance and as well as detailed guidance for taking advantage of Multi-AZ deployments to architect high-performance applications and production workloads.

Amazon Kinesis is the AWS service for real-time streaming big data ingestion and processing. This talk gives a detailed exploration of Kinesis stream processing. We'll discuss in detail techniques for building, and scaling Kinesis processing applications, including data filtration and transformation. Finally we'll address tips and techniques to emitting data into S3, DynamoDB, and Redshift.

Amazon Elastic Compute Cloud (Amazon EC2) provides a broad selection of instance types to accommodate a diverse mix of workloads. In this technical session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.

Amazon DynamoDB is a fully managed, highly scalable distributed database service. In this technical talk, we show you how to use DynamoDB to build high-scale applications like social gaming, chat, and voting. We show you how to use building blocks such as secondary indexes, conditional writes, consistent reads, and batch operations to build the higher-level functionality such as multi-item atomic writes and join queries. We also discuss best practices such as index projections, item sharding, and parallel scan for maximum scalability.

Learn how to utilize Amazon Route 53 latency-based routing, weighted round-robin, and other features in conjunction with DNS failover to direct traffic to the least latent, most available endpoints across a global infrastructure. We explore topics such as balancing traffic between endpoints in terms of load and latency, and discuss how to provide multi-record answers to improve client-side resiliency. As part of this session, Loggly will present how they utilize Route 53 for their traffic management needs.

Learn the specifics of Amazon RDS for PostgreSQL's capabilities and extensions that make it powerful. This session covers database data import, performance tuning and monitoring, troubleshooting, security, and leveraging open source solutions with RDS. Throughout, this session focuses on capabilities particular to RDS for PostgreSQL.

Almost all applications and services have a need to communicate over email. Amazon Simple Email Service (SES) enables email functionality that will scale with your business. So what should an email-sending application that integrates with SES look like? In this session we cover common patterns, architectures, and best practices that you can use to build a robust email solution that takes advantage of the SES platform.

Come learn about new and existing Amazon S3 features that can help you better protect your data, save on cost, and improve usability, security, and performance. We will cover a wide variety of Amazon S3 features and go into depth on several newer features with configuration and code snippets, so you can apply the learnings on your object storage workloads.

Get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance. Finally, we'll announce new features that we've been working on over the past few months.