[CW: I promised to update this blog post after the conference, but wrote a complete new post instead: 2012 Amazon Web Services (Health) User Conference Trip Report: Sounds Like (Nine)Teen(99) Spirit]
I’ll be/I am/I was (depending on when you read this) attending AWS re: Invent, Amazon Web Services’ first Global Customer and Partner Conference on Tuesday, Wednesday, and Thursday of this week. I’ll be looking for the healthcare angle to Amazon’s vision of cloud computing.
The conference runs (ran) from 11/27-11/29 in Las Vegas. I’ll tweet about it, so I embedded a couple tweets streams below. I’ll favorite tweets for later. After the conference I’ll replace the live tweets with a retrospective of my favorite favorites. I’ll use the http://ehr.bz/aws short URL during the conference, if you want to look up a session (deep linking to specific sessions doesn’t work, so see below). If you’re not on Twitter, stay tuned to this page. If you are on Twitter, you can follow me at @EHRworkflow.
Here are my recent tweets. During the conference they’ll likely be about the conference. Before and after (before I edit this post) you’ll see non-conference-related tweets.
Tired of listening to me? Here are recent tweets containing the #AWSreInvent hashtag. Most of these will be posted from other folks tweeting about the conference, but you may also see a couple of mine fly by. Sorry about that.
Here on some abstracts from some of the 150+ sessions that caught my eye before the conference. While AWS re: Invent is not an industry vertical-oriented health IT conference such as HIMSS and AMIA, most of the following topics have implications for computing in healthcare: cloud, workflow (or course), security, mobile, games, data science/big data, etc. I won’t necessary go to every one (Look! Something shiny!). But I’ll probably go to a lot of them. I’ll update this blog post after the conference. So check back.
Dealing with scale and concurrency in today’s web and mobile services can require complex business logic in your application. To achieve high scale in the cloud, often developers have to coordinate and track state for steps in application processes distributed across remote data centers. Come to this session to learn how Amazon Simple Workflow (SWF) manages and coordinates your application sequences in “workflows” by our AWS pay-as-you-go service. We will walk through real-world examples of customers who are basing their high-scale, fault-tolerant applications on Simple Workflow today.
There are so many different thoughts about how to secure your applications running in AWS that it can be confusing to know where to start. In this session, we cover tips, tricks, and emerging best practices for securing your applications. We discuss topics ranging from how to configure your AWS resources to options for logging and intrusion detection. Discover that running your applications in AWS gives you a great head start.
Get under the hood with Parse.com’s founder to see how they used AWS to build their mobile Platform as a Service. In this session, you learn how Parse is using a variety of AWS services including Amazon EC2, S3, ELB, EBS and Route53 to build data storage, push, and easy upload services for mobile developers.
Applications today can span on-site and off-site environments, as well as across multiple compute resources in the cloud. Come learn how to simplify your application’s state management, asynchronous tasks and work distribution with Amazon Simple Workflow (SWF). During this session, you will learn how to use the SWF Flow Framework to define your application logic in “workflows” that are managed at high-scale and with fault-tolerance by Amazon SWF.
Game developers need to spend their time building new games and features, not managing infrastructure. Meteor Entertainment has learned how-to minimize the time they spend managing infrastructure by automating deployments, monitoring systems through log analysis, and by making their data tier easy to scale. Attend this session to hear all about Meteor’s best-practices.
(Hey! I like Python.)
Learn how to configure, deploy and scale a Python application running on Amazon Elastic Beanstalk. This talk uses two samples, a simple url shortening API built using Flask, and an image processing app built using Django, to demonstrate how to quickly get up and running on Amazon Elastic Beanstalk. In addition to learning best practices, the talk covers performance tweaks, and options for scalable data storage including S3, DynamoDB and RDS.
In this talk, we dive into the Netflix Data Science & Engineering architecture. Not just the what, but also the why. Some key topics include the big data technologies we leverage (Cassandra, Hadoop, Pig + Python, and Hive), our use of Amazon S3 as our central data hub, our use of multiple persistent Amazon Elastic MapReduce (EMR) clusters, how we leverage the elasticity of AWS, our data science as a service approach, how we make our hybrid AWS / data center setup work well, and more.
The problem of big data is not only that it is capacious, but that it is also heterogeneous, dirty, and growing even faster than the improvement in disk capacity. One challenge is then to derive value by answering ad hoc questions in a timely fashion that justifies the preservation of big data. A group of us from databases, machine learning, networking, and systems just started a new lab at University of California, Berkeley, to tackle this challenge. The AMPLab is working at the intersection of three trends: statistical machine learning (Algorithms), cloud computing (Machines), and crowdsourcing (People). One of the driving applications for the AMP Lab is cancer genomics. Over the next several years, gene-sequencing technologies will begin to make their way into medicine, offering the most complex tests available. This advance brings a new type of data with tremendous promise to help elucidate physiological and pathological functions within the body, as well as to make more informed decisions about patient care. The cost of genome sequencing is projected to fall within range where it may be used for diagnostic and treatment purposes within the next two years. Due to the overwhelming amount of information returned by these tests, direct human interpretation is not feasible, and therefore will have to be guided by computational methods and visualization. The use of sequencing information has debuted in cancer. A provocative hypothesis is that the massive growth of online digital descriptions of tumor cell genomes will enable computer scientists to help make breakthroughs in cancer treatment, perhaps even within the next few years. Learn about the frightening fractions of cancer, dramatic speedups in genomic data processing by using cloud computing, and the blurring between opportunity and obligation when dealing with a problem that affects the lives of millions of people.
Learn how engineers at startups and larger enterprises use data to drive greater insight into their operations, customers, and business in this lively discussion of big data techniques and tools. From Hadoop to data warehouses, this panel discusses the tools, techniques, tips, and tricks for building data driven teams and delivering cost optimization at scale.