I bought an Apple Watch Series 2 for Christmas. Most of the motivation for getting it was the struggles I had syncing my workout data. I was using a Garmin Vivosmart watch plus a heart rate monitor to get workout data.
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Despite having an SEO hostile name, h2o.ai is a pretty cool company. They have developed a great open source plug-and-play data science platform in h2o. They some other projects that are noteworthy and of course Sparkling Water, the subject of this post. Sparkling Water is essentially the h2o APIs on top of Spark, allowing the power of h20 to take advantage of Sparks distributed computing model. That being said, is it worth it to load another dependency when Sparks MLLib is adequate for most machine learning needs? I went through this exercise a few weeks ago and this post is mostly my notes with some added illustration and some code.
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A few weeks ago I listened to a talk that really got me curious about OCaml. OCaml is a programming language I saw and kind of shrugged off many times in the past. Not because there is anything wrong with the language, just because it doesn’t (at least not overtly) play in any of the domains I play in.
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Alluxio is an open source project aimed at solving caching for analytical applications. If that doesn’t mean anything to you, then this may be the wrong post for you. Alluxio provides a way to reduce the cost of data querying (I’ll explain this later), without adding complexity of added databases or long term storage solitons.
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I’ve been trying to get more involved in DevOps related work as I’ve moved further down the tech stack in data engineering. I try to learn new tech during free time and look for the best courses that fit my requirements of being concise, informative and as hands on as possible.
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