AWS re:Invent 2016

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**AWS re:Invent 2016**

AWS re:Invent 2016 Conference

Latest Episodes:

BDA203: Billions of Rows Transformed in Record Time Using Matillion ETL for Amazon Redshift Dec 25, 2016

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Billions of Rows Transformed in Record Time Using Matillion ETL for Amazon Redshift

BDA204: Leverage the Power of the Crowd To Work with Amazon Mechanical Turk Dec 25, 2016

Show notes

With Amazon Mechanical Turk (MTurk), you can leverage the power of the crowd for a host of tasks ranging from image moderation and video transcription to data collection and user testing. You simply build a process that submit tasks to the Mechanical Turk marketplace and get results quickly, accurately, and at scale. In this session, Russ, from Rainforest QA, shares best practices and lessons learned from his experience using MTurk. The session covers the key concepts of MTurk, getting started as a Requester, and using MTurk via the API. You learn how to set and manage Worker incentives, achieve great Worker quality, and how to integrate and scale your crowdsourced application. By the end of this session, you will have a comprehensive understanding of MTurk and know how to get started harnessing the power of the crowd.

BDA205: Delighting Customers Through Device Data with Salesforce IoT Cloud and AWS IoT Dec 25, 2016

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The Internet of Things (IoT) produces vast quantities of data that promise a deep, always connected view into customer experiences through their devices. In this connected age, the question is no longer how do you gather customer data, but what do you do with all that data. How do you ingest at massive scale and develop meaningful experiences for your customers? In this session, you learn how Salesforce IoT Cloud works in concert with the AWS IoT engine to ingest and transform all of the data generated by every one of your customers, partners, devices, and sensors into meaningful action. You also see how customers are using Salesforce and AWS together to process massive quantities of data, build business rules with simple, intuitive tools, and engage proactively with customers in real time. Session sponsored by Salesforce.

BDA207: Fanatics: Deploying Scalable, Self-Service Business Intelligence on AWS Dec 25, 2016

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Data is growing at a quantum scale and one of challenges you face is to enable your users to analyze all this data, extract timely insights from it, and visualize it. In this session, you learn about business intelligence solutions available on AWS. We discuss best practices for deploying a scalable and self-serve BI platform capable of churning through large datasets. Fanatics, the nation’s largest online seller of licensed sports apparel, talks about their experience building a globally distributed BI platform on AWS, that delivers massive volumes of reports, dashboards, and charts on a daily basis to an ever growing user base. Fanatics shares the architecture of their data platform, built using Amazon Redshift, Amazon S3, and open source frameworks like Presto and Spark. They talk in detail about their BI platform including Tableau, Microstrategy, and other tools on AWS to make it easy for their analysts to perform ad-hoc analysis and get real-time updates, alerts, and visualizations. You also learn about the experimentation-based approach that Fanatics adopted to fully engage their business intelligence community and make optimal use of their BI platform resources on AWS.

BDA209: NEW LAUNCH! Introducing AWS Glue: A Fully Managed ETL Service Dec 25, 2016

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AWS Glue is a fully managed ETL service that makes it easy to understand your data sources, prepare the data for analytics, and load it reliably to your data stores. In this session, we will introduce AWS Glue, provide an overview of its components, and discuss how you can use the service to simplify and automate your ETL process. We will also talk about when you can try out the service and how to sign up for a preview.

BDA303: NEW LAUNCH! Intro to Amazon Athena. Easily analyze data in S3, using SQL. Dec 25, 2016

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Amazon Athena is a new interactive query service that makes it easy to analyze data in Amazon S3, using standard SQL. Athena is serverless, so there is no infrastructure to setup or manage, and you can start analyzing your data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3.

BDA304: What’s New with Amazon Redshift Dec 25, 2016

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In this session, you learn about the latest and hottest features of Amazon Redshift. Join Vidhya Srinivasan, General Manager of Amazon Redshift, to take a deep dive into the architecture and inner workings of Amazon Redshift. You discover how the recent availability, performance, and manageability improvements we’ve made can significantly enhance your end user experience. You also get a glimpse of what we are working on and our plans for the future.

BDM201: Big Data Architectural Patterns and Best Practices on AWS Dec 25, 2016

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The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.

BDM203: FINRA: Building a Secure Data Science Platform on AWS Dec 25, 2016

Show notes

Data science is a key discipline in a data-driven organization. Through analytics, data scientists can uncover previously unknown relationships in data to help an organization make better decisions. However, data science is often performed from local machines with limited resources and multiple datasets on a variety of databases. Moving to the cloud can help organizations provide scalable compute and storage resources to data scientists, while freeing them from the burden of setting up and managing infrastructure.

BDM302: Real-Time Data Exploration and Analytics with Amazon Elasticsearch Service and Kibana Dec 25, 2016

Show notes

Elasticsearch is a fully featured search engine used for real-time analytics, and Amazon Elasticsearch Service makes it easy to deploy Elasticsearch clusters on AWS. With Amazon ES, you can ingest and process billions of events per day, and explore the data using Kibana to discover patterns. In this session, we use Apache web logs as example and show you how to build an end-to-end analytics solution. First, we cover how to configure an Amazon ES cluster and ingest data into it using Amazon Kinesis Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data. Then we demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we dive deep into the Elasticsearch query DSL and review approaches for generating custom, ad-hoc reports.

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