TDS Newsletter: December Must-Reads on GraphRAG, Data Contracts, and More
Don't miss out on the new edition of Variableour weekly newsletter featuring a high-quality selection of editors' picks, in-depth reviews, community stories, and more.
Yes, it's 2026 — and we're already gearing up for an eventful year of growth and learning here at TDS. We also published a number of astrology articles last month, including the rise of the holiday season, and we don't want you to miss any of them.
This week, we're taking a swing at some of the last 2025, highlighting some of our most popular stories from December. Make no mistake, though: they cover timely and actionable topics in machine learning, data science, and AI, and will remain relevant for weeks and months to come.
GraphRAG Performance: How to Build Cost-Effective, High-Recall Retrieval Systems
When “vanilla” RAG systems just don't cut it, you might want to explore the power of GraphRAG — and Partha Sarkar's detailed guide is a good starting point for anyone interested in tinkering with this powerful, hybrid pipeline and potentially low-cost approach.
Six Lessons Learned to Build RAG Programs in Manufacturing
For more RAG insights, we highly recommend Sabrine Bendimerad's collection of best practices, covering data quality, testing, and more.
How to Implement Simple Data Contracts in Python for Data Scientists
Quick and to the point, Eirik Berge presents a guide to using the open source library Pandera if you intend to define schemas as class objects.
Some Highlights of December
From learning algorithms in Excel to improving Pandas performance, here are a few of the most read and shared stories from the past month.
Machine Learning and Deep Learning Series “Advent Calendar”: The Blueprint, by Angela Shi
How Agent Handoffs Work in Multi-Agent Systems, by Kenneth Leung
Reading Research Papers in the Age of LLMs, by Parul Pandey
7 Pandas Performance Tricks Every Data Scientist Should Know, by Benjamin Nweke
What Happens When You Build an LLM Using Only 1's and 0's, by Moulik Gupta
Meet Our New Writers
We hope you take the time to check out the great work from TDS contributors who have recently joined our community:
- Jasper Schroeder shared some useful takeaways from the Advent of Code programming challenge he recently completed.
- Morris Stallmann (and co-author Sebastian Humberg) provided a comprehensive, pragmatic primer on data mining (and how to get it in a timely manner).
- Alon Lanyado focuses on a different data challenge that ML scientists and practitioners often face: covariance shift.
Do your New Year's resolutions include publishing on TDS and joining our Author Payment Program? Now is the time to submit your latest draft!
