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By now we all know and have worked with Docker a bit. It’s perfect for creating a consistent environment. Running several of them at the same time is easy due to the isolated networking and file system. If you have a set of proper environments (Integration, Staging and Production), then it gets a bit more complicated as one has to integrate Docker with existing services: logging, monitoring, backup as well as deal with deployment and rolling updates.


Logging is probably the easiest to deal, as you have a variety of options supported by Docker, notably Syslog, JSON file, Fluentd…

Or how to handle lots of data with a small team

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One of the benefits of coming to a greenfield job — like when I joined Mind Candy two years ago — was that you can jump several technological steps ahead as you don’t have any legacy to deal with. Essentially we could build from scratch based on lessons learned from traditional data architecture. One of the main ones was to establish a real-time path right away to avoid having to shoehorn it in afterwards. Another was to avoid physical hardware. …

Or how to pick apart a unicorn

Looking again at the data science diagram — or the unicorn diagram for that matter — makes me realize they are not really addressing how a typical data science role fits into an organization. To do that we have to contrast it with two other roles: data engineer and business analyst.

What makes a data scientist different from a data engineer? Most data engineers can write machine learning services perfectly well or do complicated data transformation in code. …

Or why working in mobile gaming is different

If you are working as a data architect or a technical lead of a data team you are in a bit of thankless position at the moment. You could be working at or even founding one of the many data platform startups right now. Or work for the many enterprise consultancies that provide “big data solutions”. Both would mean directly profiting from you acquired technical skills. Instead, you are working in a company that actually needs the data you provide but also doesn’t care how you get it. There is the old…

Or why limiting supply is not enough to be a stable currency

I have been watching the development of Bitcoin since it started and found it fascinating. From a technical point of view the idea of cryptocurrency is intriguing. Even if perfect security can not be guaranteed, I’m not worried about Bitcoin’s long-term prospects from an algorithmic point of view. I think the real problems are economical.

I don’t doubt, as some have, that Bitcoin is a proper currency. Economics has a pretty low bar for a currency: 1) is a store of value, 2) accepted for transactions, 3) is a accounting unit, 4) in common use within a territory. If you…

Kevin Schmidt

CTO Century Tech, former head data cruncher at Mind Candy and, Speaker at O’Reilly Strata and Nucl.AI.

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