Building Scalable, Serverless Mobile and Internet of Things Back Ends
Ajay Nair, Senior Product Manager, AWS Lambda, Amazon Web Services
Oliver Klein, Solutions Architect, Amazon Web Services
AWS Lambda is a compute service that runs your code in response to events and automatically manages the compute resources for you. Together with Amazon Cognito, Amazon SNS push notifications, and Amazon DynamoDB, AWS Lambda is a powerful tool to build a highly scalable back end for your mobile or IoT applications. This session will take a practical approach to developing real-world IoT and mobile applications with AWS in which the back end is serverless and can scale virtually unlimited users without any infrastructure or servers to manage. This session is for those who want to get started quickly. It includes a review of key concepts and how the AWS SDKs make it easy to create powerful applications for an always-on world that connects beyond the desktop.
Amazon DynamoDB for Big Data
Nate Slater, Solutions Architect, Amazon Web Services
NoSQL is an important part of many big data strategies. Attend this session to learn how Amazon DynamoDB helps you create fast ingest and response data sets. We demonstrate how to use DynamoDB for batch-based query processing and ETL operations (using a SQL-like language) through integration with Amazon EMR and Hive. Then, we show you how to reduce costs and achieve scalability by connecting data to Amazon ElasticCache for handling massive read volumes. We’ll also discuss how to add indexes on DynamoDB data for free-text searching by integrating with Elasticsearch using AWS Lambda and DynamoDB streams. Finally, you’ll find out how you can take your high-velocity, high-volume data (such as IoT data) in DynamoDB and connect it to a data warehouse (Amazon Redshift) to enable BI analysis.
Implementing a Serverless AWS IoT Backend with AWS Lambda and Amazon DynamoDB (Blog Post)
Learn how to use AWS IoT rules to trigger specific device registration logic using AWS Lamba in order to populate an Amazon DynamoDB table. Read more »
Amazon Kinesis Streams Aggregators (GitHub repository)
Enables the automatic creation and visualization of aggregated time series data from Amazon Kinesis streams. Learn more »
Scaling Writes on Amazon DynamoDB Tables with Global Secondary Indexes (Ian Meyers, AWS Big Data Blog, Sep. 17th)
"To create our time series table, we create a table with a hash and range primary key, which allows us to look up an item using two discrete values." Read more »
From PostgreSQL to DynamoDB (Brad Van Vugt, sendwithus blog, May 1st)
"Our tables contained hundreds of millions of rows. Our indexes were growing exponentially and no longer fit in memory. Our write throughput was constantly bumping up against the maximum for a single database instance." Read more »