It isn’t easy, but it’s worth it.

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Photo by Avel Chuklanov on Unsplash

On December 23rd of last year, after having worked consistently with AWS for around three weeks, I took my first practice test for the DevOps Engineer Professional exam. Having scored a respectable number, I decided to dig in and do some hardcore studying for another week before deciding whether to sit the actual exam. One week later, I scheduled my exam for the 8th of January. If I passed, that would mean that in just under three weeks I had gone from relative newbie to achieving one of the two Professional tier certifications AWS offers.

As you can tell from the title, I did indeed pass the exam, and in looking back my success was at least in part due to a number of deliberate strategies that I’d like to share. Most, if not all, of these are generally applicable, in that they could just as easily be used to achieve other certifications, whether in the AWS ecosystem or others. …


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Photo by Markus Spiske on Unsplash

There is no doubt about it: the use of IoT devices (and the data they produce) are growing wildly. According to one Gartner study, by 2020 there will be 5.8 billion IoT devices online, a 46% increase from the 3.9 billion online in 2018. By 2025, the total volume of data generated by IoT devices is forecast to reach a staggering 79.4 zettabytes, which is almost six times the volume in in 2018.

The wonderful thing is that platforms like Microsoft Azure make receiving, storing, and analyzing this data quite easy. In this article, I’m going to walk you through how to harvest streamed telemetry from an IoT temperature sensing device and both store it in a data lake as well as do some rudimentary anomaly detection on it. …


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Photo by Chris Ried on Unsplash

I’ve been using the Microsoft SQL Server technology stack for more than a decade, and while I continue to be extremely bullish about it, I’ve lately changed my tune on a key component of it, namely SQL Server Integration Services, or SSIS for short. SSIS is a very powerful tool to perform extract, transform, and load (ETL) workflows on data, and can interact with pretty much any format out there. And while I’ve mostly seen it used in the context of loading data into or out of SQL Server, that certainly isn’t its only use.

I’ve authored more than my share of SSIS packages over the years, and while I still feel it’s a tremendous tool to have in your arsenal (and one that in many cases may be the only one available in large enterprises with strict standards around technology usage), I’ve now decided that for reasons I’ll outline below, I’d prefer using Python for most, if not all, ETL needs. This is especially true when combining Python with two modules specifically made for manipulating and analyzing data at scale, namely Dask and Pandas. …


Photo by Markus Spiske on Unsplash
Photo by Markus Spiske on Unsplash
Photo by Markus Spiske on Unsplash

Some time several years ago, someone asked me if I would ever consider leaving a technical role and moving into more of a management or leadership position. At the time, I thought the idea crazy; why would I give up the kind of deep, intriguing technical work I so richly enjoyed for a job that would involve at least some level of administrative overhead? I loved working through tough technical challenges, solving problems, and, as a side gig even, writing about what I learned. I felt comfortable where I was, and I had no intention to try anything else.

Then, as often happens, an opportunity arose. A new group was being formed within my organization, one that would face some unique challenges. I realized that I had a choice: I could either apply for, and possibly end up leading the new team, or I could do nothing and would almost certainly find myself as a member of it. After some consideration, I chose the former option, and, gratefully, was given the opportunity to lead the group. …


Authors note: I originally wrote this not long after the election of November 2016, and for whatever reason, never published it. Having found it languishing in my draft folder, and still very relevant today, I decided I would publish it.

In the weeks leading up to the election of 2016, I held out hope that once the day had passed, regardless of the result, we might come together as a nation and move forward. After all, our republic stands on the basis of shared acceptance of election results, even when those results do not follow our desires. And while it’s perfectly fine to grumble or complain if our chosen candidate should lose, we pick up and move on. …

About

Joshua Feierman

I love to write about all things data, tech, and personal development.

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