Fluid Thinking

All is Calm

A reflection on the personal dynamics of leadership. As leaders, we sometimes find that our character, reputation, and past successes have earned us new responsibilities, and new challenges: these are opportunities under our stewardship. Some of these will come with new teams, new staff, and new initiatives. Some of these will be strategically important to our companies and our careers.

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Blogging again, after great delay

My last published post was on October 26, 2016. That’s forever ago. A lot has happened since.

I’ve learned tremendously in the intervening 2.5 years. From these formative lessons, I’ll be publishing more on this site soon.

Among those lessons, ironically, is that one should make a habit of writing about the things they learn, fairly promptly after they have learned them. Writing is an exercise in forming thought into (hopefully) concrete language, which forces one to ponder and remember. This reinforces the neurological patterns of that learning; it’s important to actively remember at times.

Unfortunately I had set a roadblock in my own path: I was rather tired of the visual layout of my site here, and had begun work (in several failed attempts) to develop a new theme. I’ve always found the clean look of publications on Medium, but I’ve always felt leary of some of their policies. Finally, after much recent effort, I’ve crafted a new, separate Jekyll theme, specifically designed to emulate some of the feel of Medium, while publishing via GitHub pages. (Note I’m still tuning the theme a bit, based on my observations on this site.)

Therefore this post serves two purposes

It’s also come to my attention, through this process, that some of my past blogging is absolutely terrible. :thinking:

Data Model Fluidity in Analytics

The world of data, intelligence and analytics perpetually evolves, so too will the data structures we work with. With the rising popularity of data lakes, there’s a steadily increasing need for developers, consultants, and vendors to work with all kinds of data, be it structured, unstructured, or semistructured.

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Continuous Deployment with Python Packages

Streamline your Python development with GitLab CI; automate build, test, and publication of libraries to PyPI package repositories.

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