The software industry needs to learn to go slow in order to move fast

When I was young and deciding what career path would I take and when our teachers would ask what do we want to pursue, I’d always answer something along the lines of: “software engineer”. Twenty or so years ago I had much esteem for the trade. The principles of automating repetitive tasks, freeing someone from the day to day burden of repetitive chores and of course, of their salary, leaving them naked in the street, unable to feed their family, hunting for mice to survive seemed such a good investment of my time and skills.

Of course, I’m joking! Jesus! But the software industry is not and the reason that is happening is because pretty much any other industry, be it automotive, manufacturing, even sending satellites to space (and trust me, I know) is full of repetitive tasks that can be easily coded to perfection.

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Biking in Toulouse

Street market in Toulouse, over the boulevard

I haven’t had time these days to blog anymore. Since leaving my previous employer, a former “prestigious” mobile game creation company I’ve searched for other jobs, mainly going through an French telecom business, where the frustration were so high, from day one, that I quickly sought new challenges (searching again) after the first week.

Anyway, while I can’t say names and I will try not to tie this blog to any employer, directly, I’ve found a good (and exciting) job at a contractor in the space business. For which travel was included. The two weeks I’m here in Toulouse should be enough to understand 2 projects formerly under development here and to bring them over for upgrade back in my mother country (Romania).

The 2 weeks here meant that I’ve had the chance of a weekend over here. While rough at first, since the chosen hotel (MetrHotel Basso Cambo) is really well placed near the metro, the bus station directly to work, a Lidl, McDonald’s and KFC near, it took me a few days to accommodate so I didn’t have the urge to visit the center (old town) of the city.

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How about playing a little

Today I’ve left work and one of my colleagues who’s a father of two was playing Destiny 2 on Windows. Well, I’m a Linux fan and the only games I have available are the ones on the Steam OS + Linux category. Which fall short of Counter-Strike: Global Offensive (which I play in Competitive mode because public is sooo much full of d-bags) and Bioshock.

In all honesty, I just got the wake-up call that I haven’t been playing in the last 3 or 4 years pretty much anything. Funny of all, I worked in a gaming company, so games should’ve been somewhat in my target, no? Well, they haven’t, as I was too much concentrated on the day-to-day activity of the project. Which is a bummer. The last 30 minutes or so playing a T vs. CT competitive match, allthough we lost, were the most fun moments at the computer I had in a while.

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Data engineers and their unlocking potential for business use-cases

IMG_20181030_141321Nate Kupp currently holds the position of Director of Infrastructure and Data Science at Thumbtack and has presented this year his talk and success story entitled: “From humble beginnings: building the data stack at Thumbtack”. This is one of the presentations I’ve enjoyed much because it was similar to one of the pains I’ve also experienced in my day-to-day work.

A difference between Nate’s approach and mine is the executive sponsors (and a bit of luck of being in the right place, right time and the right management mentality). My experience on the other hand is, from my perspective a failure, but for others a small success against overwhelming odds.

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Bucharest’s Botanical Garden, in autumn clothing

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Relaxing pallete of colours

Yesterday my wife proposed we do a quick Sunday walk to Bucharest’s Botanical Garden in the early part of the day, right before noon around 12:00/13:00 when our little exactly 1.7 year old (today) bundle of joy goes for the mid-day nap.

I agreed for two reasons: (1) the botanical garden isn’t all that crowded and (2) it’s big enough for the baby to run around. It’s not the best of parks or recreational areas, in terms of arangements or venues, but it’s the most diverse in terms of colors and the display of plants, in comparison to other parks.

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On data architects and their cost-saving role of fitting management requests in a puzzle of infrastructure and human resources

As stated earlier, Andrey Sharapov’s presentation on “Building data products: from zero to hero!” has given me many motives to talk. And I can’t seem to stop with the ideas of things to write about. Maybe probably because I’ve been, at my current employer, at the time of writing, through the same pains as the guys at Lidl did. And those pains are centered around the management of data people, be them engineers or scientists.

The featured image of this article present a young female manager asking: “How are you?” and getting a cryptic reply: “About half a standard deviation below the mean …” which in some languages goes by as swearing or rude behaviour. But in all honesty, although English, these people come from very different backgrounds, with slight variations of vocabulary (and understanding, not that one is less of an educated person from the other as management itself is a hard discipline also as it involves a few areas of sociology, economics and more).

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Unicorn data engineers & scientists, a guide to catch, keep and sh*t rainbows

This year at CrunchConf 2018 there was an interesting talk by Andrey Sharapov an Data Engineer & Scientist at Lidl. Yes, Lidl. The store in your back alley or in your neighbourhood. Did you know it does Big Data? I assumed, yes, given one wants to optimize both the idea of minimizing waste and increasing profits (eg. how much of X do one store needs to order to ensure it’s gone by EOD).

Andrey’s talk was centered around “Building data products: from zero to hero!” and I would personally want to apraise the realism of his presentation which gives me content for more than one article on the subject. He’s one in a series of presenters at this year’s conference that has called out to the strategy of companies of investing too much in data scientists, then finding out they don’t have an infrastructure those scientists need, then trying to find data engineers a bit too late in the game (which are even more scarce than scientists).

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