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Analytics Engineer: this term has started showing up in blog posts and job listings. It all happened quickly; just a couple of years ago, it wasn't a thing our friends in the data ecosystem talked about. So how did it start trending, what is it exactly, and is it here to stay? We decided to take a closer look, and here's what we found out.
Here's our monthly roundup of relevant links for data professionals, from blog posts and tutorials to podcast episodes:
Peter Skomoroch and Mike Loukides co-authored a very interesting post on what makes product management different in the context of AI. Based on the specificities of AI software development, they make a series of recommendations for a process that also takes business priorities into account. Their post also ends with a list of relevant resources, so it is worth checking out.
“Wait, do tool X and tool Y work together? I thought they were competitive.”
There are dozens of new tools in the fast-growing data ecosystem today. Together, they are reshaping data work in exciting, productive and often surprising ways. The seeds of the data landscape for the next decade have been planted, and they’re growing wildly.
Turns out, cultivating a new ecosystem is messy.
Streamlit officially launched out of beta on October 1st, 2019 with the promise to "turn Python scripts into beautiful ML tools." On the same day, Google's AI-focused venture fund Gradient Ventures announced its investment into the startup, which has since then attracted a considerable amount of attention despite its young age.
With COVID-19 forcing almost one billion people to shelter in place around the world, many people have turned to new activities, such as drawing, baking, gardening… or online learning. If that doesn't sound like you, don't feel guilty by any means – sometimes, surviving is enough! But if you want to get more knowledgeable about data science, data engineering and artificial intelligence, we are here for you.
This is why we came up with this list of courses that can help you prepare for a future job in the data field, upgrade your existing skills, or just satisfy your personal curiosity. From free entry-level courses to full-time bootcamps, here's our selection for you to check out:
Here are 10 recent relevant links for data professionals, from blog posts and tutorials to podcast episodes:
Former Data Council speaker Will Falcon published an interesting post on PyTorch Lightning, the lightweight PyTorch wrapper born out of his Ph.D. AI research at NYU CILVR and Facebook AI Research (FAIR). Framed as "a gentle introduction", it includes a side-by-side comparison of building a simple MNIST classifier PyTorch and PyTorch Lightning, in order to illustrate how to refactor one into the other. This is highly recommended reading if you are working on AI/ML research, be it as a professional researcher, student or in production.
Your potential salary as a data engineer heavily depends on where you are based; but cost of living also varies around the world. Wondering where you can actually earn more? Let's take a closer look at the United States, Europe and Asia to compare and benchmark data engineering salaries.
Today's companies aim to be data-driven, but data infrastructure is time intensive and costly to build, maintain, and secure. A coral is the exoskeleton of a small marine animal that attaches and grows on almost anything. Once it starts growing, it can create large reefs, which support a diverse ecosystem of plants and animals. So what happens if you apply that philosophy to the world of data?
When the engineering team at Circonus began to feel the pain of systems at scale, there were some common observability tools that provided them with a firehose of operational time series telemetry. However, managing all that data, yet alone making sense of it, was extremely difficult. And the existing tools they tried for managing time series metrics either didn't give mathematical insight, or fell over at modest workloads. They needed a better solution. So they decided to look into other statistical tooling options that had proven themselves for decades in other industries.
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