All Hands on Data #78
Heat up a plate of Thanksgiving leftovers and join us for version 78 of All Hands on Data!
Data Warehouses vs. Data Lakes vs. Data Marts: Need Help Deciding?
Breaking down the differences between data warehouses, data lakes, and data marts, the author helps to understand applications and implications that go with each of these choices. If you were building these on AWS, they might be constructed using Redshift, S3, and Aurora, respectively, or, as I like to call them, "EC2 wrappers". - John Forstmeier
Friends Don't Let Friends Make Bad Graphs
Part of the job with data is presenting the information in a way that anyone can easily understand. But many times, we just throw the data into a dashboard or graph without thinking about it's interpretability. Chenxin provides loads of examples to help make your BI work more interpretable. - Blake Burch
2023 Was the Year of Large Language Models: Then and Now
This is a short and sweet overview of the LLMs introduced in 2023. It was a big year with a few models I had not heard of. With all the upheaval around OpenAI, I'm definitely curious what this landscape will look like next year. - Katt Baum
Python: Key Differentiator for Advanced Data Analytics
This article discusses the perks and future of python and how it relates to the data science community - Johnathan Rodriguez
Immutability for Data Engineers
This article addresses something that is more or less an afterthought: data immutability. If you're writing data pipelines in Python or even SQL, immutability may be more of a stylistic choice than an actual hard-set constraint. I agree that avoiding inline modifications produces more readable code, and potentially easier debugging scenarios, and I echo the closing statement: "Data Engineers who approach designs and models with an immutable mindset first ⦠will build far more hardened and useful systems in the long run" - Wes Poulsen
Maximizing business value with ETL for Big Data
Extract, Transform, Load processes are indispensable with the vast volumes of data generated at every moment to better help customer behaviors, follow market trends, and increase the efficiency of operations. ETL has become the backbone of the data engineering space, and is crucial in converting raw data into easily digestible information. In a time where every piece of data is crucial for business, ETL is there to help maximize your business! - Reed Cowan