So this is day three of the AWS re:Invent conference, and while there’s still one more day to go, I thought I’d share some casual observations.

First, Mobility

I had intended go to a lot of sessions on mobility, but soon abandoned the plan. Why? The few I attended didn’t really have to do with mobile. Yes they applied to mobile, but they were really about introducing good practices onto mobile development. For example, the session on scaling a mobile app, was basically the same presentation as the standard scaling model, but with a picture of a phone and tablet at the top. So based off of these brief encounters, I’ve developed the following rules for mobile apps.

1. Architecture matters.
2. Plan your back end services (yes services).
3. Architecture matters.
4. Test fast, often and deep.
5. See rules 1 and 3.

All joking aside I think this is truly indicative of the fact that the mobile space, particularly on AWS, has brought a lot of very talented mobile developers into the market. However; many of these talented folks don’t have backgrounds in scalable back-end architectures, and in some cases they don’t have much experience with back-ends at all. Amazon enables them to get up and running with simple tools on the back end and sample code they can modify, without always knowing how to cope if they end up with a successful application.

My take, based on some of new features begin released, is that Amazon AWS is trying to address this in two ways. One, by enticing mobile developers to attend sessions they might not normally attend, by using a picture of a phone and adding the work “mobile” to the title. Two, by giving them tools such as the new javascript APIs and gaming tools/frameworks such as leaderboards, achievements and A/B testing for mobile. While the rebranding of the sessions is a bit annoying, I have to give AWS kudos for trying to help the one-person and small teams develop better themselves and their applications. As for the new tools and frameworks, the nice thing is that they benefit both the initiated as well as the un-initiated.

Second, Big Data

I know it’s a buzz word, but I promise it’s more than that. So while I haven’t been going to many mobile sessions, I have attend a lot of session centered around Elastic Mar Reduce (EMR), and how companies such as Netflix process their analytics on AWS. A lot of these tools are in the Hadoop space, though they are certainly not limited to just using Hadoop and it’s tool set. If you’re interested in the details please check out the docs and watch the sessions once they’re available. All good stuff.

What I’d like to focus on here is the interest I observed from folks in these sessions. While no session has been empty, many of these sessions have been pretty full, especially the session on Kinesis (“real-time processing of streaming data at any scale.”). Hardly surprising as it was just announced today, but still indicative that many businesses are facing these types of problems. After a few questions in the Q & A it becomes obvious that these aren’t people with idle curiosity, they’re struggling with real business problems. I think one of the best quotes I’ve heard was from Bob Harris the CTO of Channel 4 in the UK. “The data warehouse is like looking in the rearview mirror of your car, while analytics is your heads up display.” (I hope Mr. Harris won’t be upset if I’ve gotten this quote a bit off.) This analogy really struck a cord with me. While this has always been the goal of BI, it seems that BI for the common company has been to spend an exorbitant amount of time adjusting the rearview mirror in order to program the navigational system. I don’t mean to diminish the importance of this, but in the wild west of the web, users can change their focus and decision parameters from day to day. Having a long term vision is vital, but knowing what will make the sale right now, can make the difference between folding up, or being able to realize that vision.

It was invigorating to see so many companies interested in this trend. In recent years they’ve begun to focus more on what’s happening right now, and tools like Tableau, EMR and Kinesis are helping with this. The great thing is that these tools can help not only the big corporation with a team of data scientists, but also the smaller company with a few dedicated, knowledgeable folks.