All Hands on Data #46

This week's All Hands on Data includes something for everyone in the data space. Check out our team's favorite articles from the week below!

The Chaos Data Engineering Manifesto: Spare The Rod, Spoil Prod

In a truly horrifying post, the author describes principles and approaches for implementing “data chaos engineering”. I’m truly terrified because I definitely feel like he’s onto something here.- John Forstmeier

Data observability- the rise of the data guardians

This article highlights the importance of understanding the quality of data as opposed to just collecting it. It also discusses the changing landscape, including tool consolidation to ensure folks get good data. This report is a great thought piece for folks in data. - Angel Catalan

Tips for Scholarly Research Publication

Here’s a nice succinct read for you grad students out there. As a former employee higher ed employee, one stressful experience students (and colleagues) faced was getting through theses or dissertations. Shrestha’s approach… think of your paper as a startup! The same elements that make a successful startup, make a paper worth publishing. - Katt Baum

The Data Engineer is dead, long live the (Data) Platform Engineer

Data roles are in continual flux, with titling struggling to keep up with the actual work being done. Robert believes that the role of a data engineer will be shifting to have a wider focus - ensuring that tools and platforms are spun up to facilitate data work across domain specific teams, rather than focusing all of their efforts on setting up and building pipelines. As we get easier to use tools, I definitely see the shift starting to occur. - Blake Burch

18 Ultimate Cheatsheets for Data Scientists, ML engineers and Analysts

The line I heard most from my students in my math classes back when I taught was, “Why do I have to learn this? I can just Google it.” I always had a hard time arguing with them as I knew that was true. To save you some Google time, here are some great cheat sheets that cover a large area of the data ecosystem. - Steven Johnson

Reshaping Financial Processes with AI-based Data Extraction

AI is becoming the new normal to help with tasks that are on a grand scale. Financial Planning takes a lot of work to evaluate taxes, data models, trends, profit calculations, and more. It’s interesting to think that as time and technology progresses, investing strategies and funding of accounts can possibly all be done by AI. Whether or not this has positive or negative consequences on a larger scale might be interesting to see. - Reed Cowan

DataLang: A New Programming Language for Data Scientists…Created by ChatGPT?

While this is purely a thought exercise and hypothetical, I found this interesting for a couple reasons. First, it is wildly impressive to see ChatGPT generate a custom language from scratch, and be able to fine tune it based off of feedback. Secondly, this got me thinking of some of the qualities and capabilities that make a language successful in the data ecosystem (strong data structures that support analysis, lazy evaluation, good interop, and readable syntax) - Wes Poulsen

Future of Education: Application not Regurgitation of Knowledge - Part II

I already shared the first part of this three part series. This second part introduces 3 additional changes that need to happen in education with the introduction of AI into our world. - Jon Davidson