Five Links: A Little Bit of This Edition

Happy Friday, everyone!

I’ve still been running through year-end retrospectives and new year predictions, and I haven’t hit on a theme yet. (The character of 2017 is still enigmatic.) As always, though, there are lots of good things on the interwebz, and this week covers the gamut — containers, big data, machine learning, and Alexa.


The Most Popular Language for Machine Learning and Data Science (KDNuggets, Jean-Francois Puget at IBM)

This isn’s purely scientific, but it’s an interesting trend. It looks at job listings on and, based on keywords in the listings, looks at the frequency and trajectory that specific languages are required for data science jobs. The top two contenders are Python and Java, but it also notes the insanely fast growth in interest in the Java-friendly Scala.


How Voice Technology Is Transforming Computing (The Economist)

This is worth reading all the way through, but it proposes a realistic question — what is the nature of interfaces when you can interact with a system through your own voice? From a UX perspective, this is an earth-altering change, and the developments in consumer-based machine learning and AI (like Alexa) will be important to watch to see how they affect our interactions with technology.


Looking Ahead to 2017 for OpenShift (Red Hat OpenShift Blog)

Even if you’re not using OpenShift in your IT environment (which you should!) this is a solid rundown of the trends for containers both in technology and in IT culture. Probably one of the most far-reaching is the first — looking at the interactions of multiple industries to create a single customer experience. (In this example, looking at the entire travel process, from researching ideal destinations to being greeted at the airport by a car service, instead of just “buying a ticket.”)


Data Architecture Approach for As-Is IT Landscapes (Shreyansh Sha on LinkedIn Pulse)

This hits all my high notes — architecture, technology, and a fear of change. Plus – diagrams. This is long, but easy to follow. It’s one thing to design a new architecture in a greenfield, but it is infinitely more complex to try to re-architect an existing system. This uses an open framework to look at an existing architecture, with two main actions: identify and define.

Reanalyzing the State of Java EE (John Clingan, personal blog but Red Hat product manager)

This is another long one, but a solid read and also about architecture (and Java) which are perennial favorites. This looks at a Gartner report on the future of Java EE and evaluates their arguments for and against. It also looks at Java EE applications within the context of critical technology shifts like microservices architectures and cloud-native applications.


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