AWS re:Invent 2016

You can significantly reduce database licensing and operational costs by migrating from commercial database engines to Amazon Aurora or Amazon RDS for PostgreSQL. In addition to cost reduction you also gain flexibility and operational efficiency by avoiding the frustrating usage constraints that the commercial databases licenses come with. Amazon Aurora and Amazon RDS for PostgreSQL are fully managed database services so you no longer need to worry about complex database management tasks. You can launch a single database instance or thousands of them in just a few minutes, and pay only for what you use. In this session we will dive deep into how AWS Database Migration Service and AWS Schema Conversion Tool help you migrate your commercial databases like Oracle and Microsoft SQL Server to Amazon Aurora or Amazon RDS for PostgreSQL easily and securely with minimal downtime.

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. It is purpose-built for the cloud using a new architectural model and distributed systems techniques to provide far higher performance, availability and durability than previously possible using conventional monolithic database architectures. Amazon Aurora packs a lot of innovations in the engine and storage layers. In this session, we will do a deep-dive into some of the key innovations behind Amazon Aurora, new improvements to Aurora's performance, availability and cost-effectiveness and discuss best practices and optimal configurations.

Explore Amazon DynamoDB capabilities and benefits in detail and learn how to get the most out of your DynamoDB database. We go over best practices for schema design with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others. We explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including JSON document support, DynamoDB Streams, and more. We also provide lessons learned from operating DynamoDB at scale, including provisioning DynamoDB for IoT.

Amazon RDS allows customers to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you up to focus on your applications and business. Amazon RDS provides you six database engines to choose from, including Amazon Aurora, Oracle, Microsoft SQL Server, PostgreSQL, MySQL and MariaDB. In this session, we take a closer look at the capabilities of RDS and all the different options available. We do a deep dive into how RDS works and the best practises to achive the optimal perfomance, flexibility, and cost saving for your databases.

In this session, we provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. See common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.

Which database is best suited for your use case? Should you choose a relational database or NoSQL or a data warehouse for your workload? Would a managed service like Amazon RDS, Amazon DynamoDB, or Amazon Redshift work better for you, or would it be better to run your own database on Amazon EC2? FanDuel has been running its fantasy sports service on Amazon Web Services (AWS) since 2012. You will learn best practices and insights from FanDuel’s successful migrations from self-managed databases on EC2 to fully-managed database services.

Redis Labs' CMO is hosting a fireside chat with leaders from multiple industries including Groupon (e-commerce ), Intuit (Finance ), and LifeLock (Identity Protection ). This conversation-style session will cover the Big Data related challenges faced by these leading companies as they scale their applications, ensure high availability, serve the best user experience at lowest latencies, and optimize between cloud and on-premises operations. The introductory level session will appeal to both developer and DevOps functions. They will hear about diverse use cases such as recommendations engine, hybrid transactions and analytics operations, and time-series data analysis. The audience will learn how the Redis in-memory database platform addresses the above use cases with its multi-model capability and in a cost effective manner to meet the needs of the next generation applications.

We’ll share an overview of leveraging serverless architectures to support high performance data intensive applications. Fulfillment by Amazon (FBA) built the Seller Inventory Authority Platform (IAP) using Amazon DynamoDB Streams, AWS Lambda functions, Amazon Elasticsearch Service, and Amazon Redshift to improve results and reduce costs. Scopely will share how they used a flexible logging system built on Kinesis, Lambda, and Amazon Elasticsearch to provide high-fidelity reporting on hotkeys in Memcached and DynamoDB, and drastically reduce the incidence of hotkeys. Both of these customers are using managed services and serverless architecture to build scalable systems that can meet the projected business growth without a corresponding increase in operational costs.

Quantcast provides its advertising clients the ability to run targeted ad campaigns reaching millions of online users. The real-time bidding for campaigns runs on thousands of machines across the world. When Quantcast wanted to collect and analyze campaign metrics in real-time, they turned to AWS to rapidly build a scalable, resilient, and extensible framework. Quantcast used Amazon Kinesis streams to stage data, Amazon EC2 instances to shuffle and aggregate the data, and Amazon DynamoDB and Amazon ElastiCache for building scalable time-series databases. With Elastic Load Balancing and Auto Scaling groups, they are able to set up distributed microservices with minimal operation overhead. This session discusses their use case, how they architected the application with AWS technologies integrated with their existing home-grown stack, and the lessons they learned.

Toyota Racing Development (TRD) developed a robust and highly performant real-time data analysis tool for professional racing. In this talk, learn how we structured a reliable, maintainable, decoupled architecture built around Amazon DynamoDB as both a streaming mechanism and a long-term persistent data store. In racing, milliseconds matter and even moments of downtime can cost a race. You'll see how we used DynamoDB together with Amazon Kinesis and Kinesis Firehose to build a real-time streaming data analysis tool for competitive racing.