AWS re:Invent 2016

Latest Episodes:

STG303: Deep Dive on Amazon S3 Dec 25, 2016

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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.

STG306: Tableau Rules of Engagement in the Cloud Dec 25, 2016

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You have billions of events in your fact table, all of it waiting to be visualized. Enter Tableau… but wait: how can you ensure scalability and speed with your data in Amazon S3, Spark, Amazon Redshift, or Presto? In this talk, you’ll hear how Albert Wong and Srikanth Devidi at Netflix use Tableau on top of their big data stack. Albert and Srikanth also show how you can get the most out of a massive dataset using Tableau, and help guide you through the problems you may encounter along the way.

STG307: Case Study: How Prezi Built and Scales a Cost-Effective, Multipetabyte Data Platform and Storage Infrastructure on Amazon S3 Dec 25, 2016

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Prezi has over 75 million registered users generating 900 GB of new data each day. They store XML describing over 260 million public presentations that have been viewed over 2 billion times. This created a scale problem that on-premises storage couldn't solve. In this session, you'll learn how Prezi leveraged the power and flexibility of Amazon S3 to turn their storage problem into an analytics opportunity and scale storage to meet the demands of their business without overspending. Prezi provides insight on how a small team of engineers accepted the challenge and succeeded using AWS with managed Hadoop (Amazon EMR) and Amazon Redshift. You'll see how Prezi runs and improves its data platform in a self-service data culture with this small team, without drowning in maintenance and support. Additionally, Prezi shows how the infrastructure team architected their services for storing and serving crucial data, user-generated and otherwise, using S3 and Amazon CloudFront—including pitfalls, best practices, cost considerations, and learnings along the way.

STG308: Case Study: Analytics Without Limits. FINRA’s Scalable Big Data Architecture on S3 Dec 25, 2016

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FINRA partnered with AWS product teams to leverage Amazon EMR and Amazon S3 extensively to build an advanced analytics solution. In this session, you'll hear how FINRA implemented a data lake on S3 to provide a single source for their big data analytics platform. FINRA ingests 75 billion records each day of stock market transactions, with an AWS storage footprint of 20 petabytes across S3 and Amazon Glacier. To deal with this workload, FINRA has architected a platform that separates storage from compute to manage capacity for each independently, leading to improved performance and cost effectiveness. You'll also learn how FINRA was able to leverage Hbase on Amazon EMR to achieve significant benefits over running Hbase on a fixed capacity cluster. FINRA was able to implement a system that seamlessly scales in response to data growth and can scale quickly in response to user traffic. By working with multiple clusters, FINRA can now isolate ETL and user query workloads and has achieved rapid, built-in disaster recovery capability by leveraging data storage on S3 to run from multiple AZs and across regions.

STG309: Case Study: How Startups Like Smartsheet and Quantcast Accelerate Innovation and Growth with Amazon S3 Dec 25, 2016

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Startups around the world use AWS services to access the power of the cloud to grow faster and more cost effectively. In this session, Smartsheet talks about how they were able to cost-effectively build their prototype for scale and avoid replatforming at different points in the adoption curve, and Quantcast discusses how they are running a high-performance analytics solution on AWS. They provide several tips and tricks for S3, and show how they removed a traditional MySQL data store from a distributed-image hosting application so that the only required data store is S3. They also show how to avoid common, cumbersome database practices by working with the eventually consistent nature of S3 objects and the fact that objects and directories share the same namespace.

STG311: Case Study: How Videology and Zendesk Modernized Their Big Data Platforms on Amazon EBS Dec 25, 2016

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The companies Videology and Zendesk both had the same problem—how to rearchitect their big data processing platforms to scale to meet growing demand, while at the same time improving performance, availability, and cost structure? Videology provides a converged advertising solution that is screen-agnostic, ensuring unduplicated reach with the right frequency cadence to achieve guaranteed results. To achieve this, their big data platform ingests, processes, and analyzes a variety of logs. In this talk, Videology discusses how they migrated our platform to use Cloudera on a mix of m4 and r3 instances using the Amazon EBS Streaming Optimized HDD (st1) volume type. Zendesk provides a cloud-based customer support platform that allows quicker and easier interaction between businesses and customers. To deliver this experience, Zendesk runs a large Elasticsearch, Logstash, Kibana stack. The talk also discusses how Zendesk rearchitected their deployment to use m4s and also leverage the EBS Streaming Optimized HDD (st1) volume type. Tips for success will be shared throughout.

SVR201: NEW LAUNCH! Serverless Apps with AWS Step Functions Dec 25, 2016

Show notes

AWS Step Functions is a new, fully-managed service that makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Step Functions is a reliable way to connect and step through a series of AWS Lambda functions so that you can build and run multi-step applications in a matter of minutes. This session shows how to use AWS Step Functions to create, run, and debug cloud state machines to execute parallel, sequential, and branching steps of your application, with automatic catch and retry conditions. We share how customers are using AWS Step Functions to reliably scale multi-step applications such as order processing, report generation, and data transformation–all without managing any infrastructure.

SVR202: What’s New with AWS Lambda Dec 25, 2016

Show notes

In this session, you'll learn what’s new and hot with AWS Lambda. Come on a tour with Dr. Tim Wagner, General Manager of AWS Lambda, to learn what we’ve been working on and what we are planning for the future. You'll get a hands-on demonstration of some our newest features which will provide you with a launching pad for some of the later sessions in the day.

SVR301: Real-time Data Processing Using AWS Lambda Dec 25, 2016

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Serverless architecture can eliminate the need to provision and manage servers required to process files or streaming data in real time. In this session, we will cover the fundamentals of using AWS Lambda to process data in real-time from push sources such as AWS Iot and pull sources such as Amazon DynamoDB Streams or Amazon Kinesis. We will walk through sample use cases and demonstrate how to set up some of these real-time data processing solutions. We'll also discuss best practices and do a deep dive into AWS Lambda real-time stream processing.

SVR304: bots + serverless = ❤ Dec 25, 2016

Show notes

Chatbots are the new apps. Businesses of all sizes, from startups to enterprises, are looking for new ways to connect with their users through natural, conversational interfaces. Developers have started using chatbots to improve the productivity and efficiency of their operations. In this session, we show how to use AWS Lambda and other serverless offerings from AWS to build chatbots quickly and efficiently. We share examples from our recently concluded AWS Slack Hackathon with a full walkthrough of building a conversational chatbot in an easy, fast, and fun way, along with helpful tools, tips, and techniques.

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