AWS re:Invent 2017

Latest Episodes:

SRV315: How We Built a Mission-Critical, Serverless File Processing Pipeline for over 100 Million Photos Dec 01, 2017

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In this session, principal architect Mike Broadway describes how HomeAway built a high-throughput, scalable pipeline for manipulating, storing, and serving hundreds of image files every second with Lambda, Amazon S3, DynamoDB, and Amazon SNS. He also shares best practices and lessons learned as they scaled their mission-critical On Demand Image Service (ODIS) system into production. Lambda functions form the backbone of ODIS, which handles over 100 million photographs that are uploaded to HomeAway's vacation rental platform. HomeAway is a vacation rental marketplace with more than 2 million rentals in 190 countries and is part of Expedia.

SRV316: How Agero is Preventing and Detecting Vehicle Accidents in Real-Time with Serverless Computing Dec 01, 2017

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To address rising crash fatalities, Agero built a near real-time driver behavior analysis platform that provides actionable insights to its customers on how to become safer drivers. Come learn how Agero built this responsive, scalable platform using an entirely serverless mobile backend. The backend is built on Lambda, DynamoDB, Amazon S3, Kinesis, and Amazon Redshift. Agero protects 80 million motorists in North America, almost one in three vehicles on the road today, through their software-enabled driver safety services.

SRV317: Unlocking High Performance Computing for Financial Services with Serverless Compute Dec 01, 2017

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AWS helps financial services institutions run risk and pricing scenario calculations against large datasets in shorter timeframes and at lower cost. In this session, we will discuss how high performance computing (HPC) and grid computing patterns in the cloud are evolving to leverage serverless architectures with AWS Lambda. Also in this session, Fannie Mae discusses how it migrated a mission-critical, financial modeling application to Lambda from an on-premises grid computing infrastructure. It will describe the journey to serverless computing to develop the first serverless high performance computing (HPC) platform in its industry. Fannie Mae will also cover how Lambda has enabled the company to reliably perform quadrillions of calculations each month, at a fraction of the cost and effort.

SRV318: Research at PNNL: Powered by AWS Dec 01, 2017

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Pacific Northwest National Laboratory's rich data sciences capability has produced novel solutions in numerous research areas including image analysis, statistical modeling, and social media (and many more!). See how PNNL software engineers utilize AWS to enable better collaboration between researchers and engineers, and to power the data processing systems required to facilitate this work, with a focus on Lambda, EC2, S3, Apache Nifi and other technologies. Several approaches will be covered including lessons learned.

SRV319: How Nextdoor Built a Scalable, Serverless Data Pipeline for Billions of Events per Day Dec 01, 2017

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In this session, learn how Nextdoor replaced their home-grown data pipeline based on a topology of Flume nodes with a completely serverless architecture based on Kinesis and Lambda. By making these changes, they improved both the reliability of their data and the delivery times of billions of records of data to their Amazon S3–based data lake and Amazon Redshift cluster. Nextdoor is a private social networking service for neighborhoods.

SRV335: Best Practices for Orchestrating AWS Lambda Workloads Dec 01, 2017

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Serverless and AWS Lambda specifically enable developers to build super-scalable application components with minimal effort. You can use Amazon Kinesis and Amazon SQS to create a universal event stream to orchestrate Lambdas into much more complex applications. Now, using AWS Step Functions, we can build large distributed applications with Lambdas using visual workflows. See how Step Functions are different from Amazon SWF, how to get started with Step Functions, and how to use them to take your Lambda-based applications to the next level. We start with a few granular functions and stitch them up using Step Functions. As we build out the application, we add monitoring to ensure that changes we make actually improve things, not make them worse. Leave the session with actionable learnings for using Step Functions in your environment right away. Session sponsored by Datadog

SRV336: Build a Serverless, Face-Recognizing IoT Security System with Amazon Rekognition and MongoDB Stitch Dec 01, 2017

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Learn how to build powerful backends without managing servers by using MongoDB Stitch. Stitch is a backend-as-a-service that lets developers perform CRUD operations directly against their database with a REST API, declaratively specify field-level security on their data, and compose server-side logic and external services with hosted functions. We provide four live coding demonstrations of Stitch in action. First, we demonstrate querying and inserting data into Stitch by adding comment capability to a static blog. Second, we demonstrate the power of Stitch's declarative ACL rules in the context of a medical records application. Third, we show services integration using Amazon S3 and Amazon Rekognition. Finally, we put it all together with an IoT-powered two-factor door security system, demonstrating how Stitch orchestrates a complex architecture of devices, logic, and services. Session sponsored by MongoDB

SRV401: Become a Serverless Black Belt: Optimizing Your Serverless Applications Dec 01, 2017

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Are you an experienced serverless developer who wants a handy guide to unleash the full power of serverless architectures for your production workloads? Do you have questions about whether to choose a stream or an API as your event source, or whether to have one function or many? In this talk, we discuss architectural best practices, optimizations, and handy little cheat codes to build secure, high-scale, high-performance serverless applications, using real customer scenarios to illustrate the benefits.

SRV402: Big Data, Analytics and Machine Learning on AWS Lambda Dec 01, 2017

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AWS Lambda is a great fit for many data processing tasks, for data analytics and for machine learning inference. The Lambda team use Lambda for our own Analytics in conjunction with other AWS services. In this session, we will cover how we tie these services together to crunch the data Lambda creates to generate insights to better run our service. We will cover common design patterns for big data processing, how they map to Lambda and serverless, and look at some new patterns that serverless makes possible. Finally we will look to how to leverage Machine Learning inference on Lambda to derive better insights from the data.

STG201: Storage State of the Union Dec 01, 2017

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In this session, learn about all of the AWS storage solutions, and get guidance about which ones to use for different use cases. We discuss the core AWS storage services. These include Amazon Simple Storage Service (Amazon S3), Amazon Glacier, Amazon Elastic File System (Amazon EFS), and Amazon Elastic Block Store (Amazon EBS). We also discuss data transfer services such as AWS Snowball, Snowball Edge, and AWS Snowmobile, and hybrid storage solutions such as AWS Storage Gateway.

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