All Hands on Data #8
Happy Wednesday! Take a break from Prime Day shopping and check out these article recommendations from the team.
Data and System Visualization Tools That Will Boost Your Productivity
The author provides a collection of tools that provide pain points both for new and experienced data workers and lists resources and approaches to make interacting with them easier. One that stood out was the way you can "beautify" your Git console output without having to resort to a full GUI. The console lovers will be pleased. - John Forstmeier
Building quality from inside out
Petr touches on the idea of reducing the amount of "fire-fighting" a data team does by taking steps to reduce "accidental complexity". This unintended complexity arises from trying to quickly accomplish requests rather than taking the time to architect a quality structure. From my experience, I know it's easy to focus on knocking out tasks without contextualizing the changes - but this is a great reminder why that's a bad practice if you don't want a world of pain years down the line. - Blake Burch
How to Become a Data Engineer
Drives home the importance of the data engineering role, and provides some introductory definitions and jump off points. - Eric Elsken
Using Data Science to Uncover the Work of Women in Science
I recently spoke with a doctor of library science about the challenges one faces when digitizing old records. In the pursuit to accurately catalogue Smithsonian's records, Dr. Elizabeth Harmon and her team used data science to discover that many more women worked in science in the early 1900s than anyone thought. - Katt Baum
Why rising cloud costs are the silent killers of data platforms
Cloud costs rarely get talked about in the data space, so I appreciated the candor Kris had here. While it's easier than ever to set up a data stack with modern tooling, teams need to focus more on tracking and managing their costs unless they eventually want to run out of budget. At the same time, for tools to be truly helpful, they need to provide better systems to minimize cost. - Blake Burch
Data Observability Vs Data Quality: What makes them different?
In my experience in the data field, data observability and data quality have been used interchangeably. This article outlines what these two terms mean and why they should not be used as synonyms. - Steven Johnson