All Hands on Data #70
Did you catch a slight breeze in the air this week? We hope this edition of AHoD helps you fall into the world of data.
The Fancy Data Stack - Batch Version
The term "data stack" is in as much flux as the overall data space - are we in the modern data stack or post-modern data stack or the anti-post-ultra-modern data stack? Whichever it is, the author provides a simple breakdown of one possible architecture but also lists a ton of cool open source alternatives for each stage. I'm always onboard with open source. - John Forstmeier
Data Warehouses vs. Operational Data Stores vs. Data Lakes - How to Store Your Data for Analytics
Jargon and barely differentiated terminology are growing exponentially. There's certainly value in new tools and technologies. What I don't find useful is spending time trying to understand slight differences versus working on and in the data, ultimately aligning solutions based on each companies specific needs and circumstances. - Shawn Fergus
Demystifying the Large Language Models (LLMs)
This article breaks down the largely complex LLMs into simple understandable components. It also explores some of the "guts" of these models so you can see what happens under the hood. - Wes Poulsen
Future of Work Report: AI at Work
LinkedIn investigated the impacts of AI in the workforce. They give a pretty comprehensive and insightful look into how AI is already changing the skills employees are acquiring and employers are seeking. - Katt Baum
4 Keys to Innovative Data and Analytics
This article touches on combining the worlds of SaaS/PaaS in data analytics, while also leveraging open-source frameworks. With the shift in the economic conditions, it's important to ensure data is delivering value and this article walks through how to look at data analytics from a different perspective. - Angel Catalan
NP-What? Complexity Types of Optimization Problems Explained
I occasionally revisit or stumble upon the notion of NP-Complete problems and what they mean. However, this is by far the most understandable breakdown of the differences between P, NP, and more complex problems. Even better is the simple descriptions of Big O classifications. - Eric Elsken