As a founder and CTO, one of the biggest surprises I encountered was how different my working relationship became with my former technical peers. A casual opinion about a technical matter, which used to be routine, became very problematic when I acquired status due to my title. And my schedule made it even harder for my team to work with me. I struggled to communicate with them, and they with me, about things that used to be fluid and easy. But in the process I also worked with some people who seemed to navigate and help me navigate this new relationship much more easily.

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Do you know what database indexes are and how they work? Do they seem hard to understand? They don’t have to be. The basic principles you need to know are simple and easy to remember.

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Chaos Engineering: Building Confidence in System Behavior through Experiments. O’Reilly Media, 2017. Casey Rosenthal, Lorin Hochstein, Aaron Blohowiak, Nora Jones, and Ali Basiri.

This book should be required reading for anyone building a system complex enough to make it hard to understand the full scope of its behaviors under varying circumstances, which describes most software.

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I gave this talk at a VividCortex-hosted event including customers and friends. The talk is a synopsis of part of an internal dossier I’m building at VividCortex, summarizing current trends in our industry and making some predictions about their implications. I’ve been validating my quasi-research by discussing it in-depth with our customers and with experts from a spectrum of technology industry, and used this opportunity to share what I’ve learned from those conversations.

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I was invited to MongoDB’s New York headquarters to kick off a series of internal tech talks on performance, with a presentation on system performance theory. The talk began by building intuition about Little’s Law, segued into queueing theory, and rounded out with the Universal Scalability Law.

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Leveraging distributed data and state is today’s key competitive advantage. This track explored the technical challenges and lessons learned in managing distributed state in large-scale applications that reliably process millions of events per second. Attendees learned proven strategies and gained new insights from leading practitioners into how to handle real-time data in streams and events.

I was a program committee member, and the track host for the Distributed Data track on Tuesday. I did not present at this conference, but I composed remarks to help weave the presentations in this track into a cohesive narrative throughout the day. What follows is a lightly edited version of my introductory remarks for the day’s sessions, as well as introductions to each session. If you’re interested in viewing slides or video of the sessions, you can purchase the video collection directly through O’Reilly Safari. Speakers may also post their slides, which are often linked from the session pages.

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I’ve spent a significant portion of my career working in companies that are at least partially remote/distributed. One of them was globally distributed, with no central headquarters at all. Everyone worked from their homes. When I founded VividCortex, I wanted to blend the best of both worlds, and to my credit, some of what I did has been an improvement. There are still downsides, though, and in this post I want explore how distributed teams can address a basic human need: seeing each other in-person.

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I wrote previously about configuring Chrome for privacy and convenience, to ensure that cookies and history don’t accumulate in the browser. I recently decided to switch from Chrome to Firefox. Here’s how I’ve configured it to behave in a similar fashion, which I believe helps protect my privacy.

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For various reasons, I’ve become interested in analytical databases. These are traditionally called OLAP (online analytics processing) and are designed to extract insights from very large datasets, often with expectations of long response times (hours). More recently, though, various databases capable of running relatively interactive queries over large datasets have emerged. This post is more-or-less a list of analytic databases, with somewhat of a taxonomy added.

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