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Once a week, we write this email to share the links we thought were worth sharing in the Data Science, ML, AI, Data Visualization, and ML/Data Engineering worlds.
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And now…let’s dive into some interesting links from this week.
Why Are Rivers So Mathematical?
A simple scaling law brings order to the chaos of flowing water, rock, and sediment. New findings have extended the law even further…In 1957, a U.S. Geological Survey scientist named John Hack discovered the most important law of river networks. In rivers and streams in Virginia and Maryland, Hack measured the length of each stream and the area of the land that slopes toward that stream and therefore drains into it, called its basin or drainage area. What he discovered is now known as Hack’s law: Any stream, from the littlest brook to the mightiest river, has a length that’s proportional to its drainage area raised to the power of 0.6. (In symbolic form: L ~ A0.6.)…
Against SQL
The relational model is great…But SQL is the only widely-used implementation of the relational model, and it is: Inexpressive, Incompressible, Non-porous…This isn’t just a matter of some constant programmer overhead, like SQL queries taking 20% longer to write. The fact that these issues exist in our dominant model for accessing data has dramatic downstream effects for the entire industry:…The core message that I want people to take away is that there is potentially a huge amount of value to be unlocked by replacing SQL, and more generally in rethinking where and how we draw the lines between databases, query languages and programming languages…
The night the earth shook, strangers started to draw
On the morning of February 6, 2023, an earthquake killed more than fifty thousand people in Türkiye and Syria. I spent a week inside the edit history of the world’s free map, reading what the internet did that morning. I haven’t stopped thinking about it…
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You could have invented PageRank
Picture this, the year is 1996. You find yourself frustrated with the incumbent search engines like AltaVista, which primarily does a content-based search (it’ll give you an article on “Hotels for Chickens” if you search “Hotels” because the word matches). There’s gotta be a better way, right? Well, in hindsight, of course. Sergey Brin and Larry Page came up with this precise algorithm, i.e., PageRank, which was one of the key algorithms that helped catapult Google into a household name and made them tons of money. Both Sergey and Larry were grad students at Stanford, so their coming up with such an amazing algorithm doesn’t seem surprising. However, the question is, could you have stumbled upon the same? I think yes…Last month we advertised three roles: a senior data analyst (£55k), a mid-level data analyst (£40k) and a data engineer (£55k). Not amazing money, but a great pension, just one day a month in the office, lots of annual leave and actual stability…The jobs were live for one week. The senior DA and DE got around 300 applicants each, the mid DA got around 140…Sounds brutal, but here’s the breakdown.….
ICLM 2026 Tutorial Videos
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning…BtrLog: Low-Latency Logging for Cloud Database Systems
We present BtrLog, a reusable cloud logging service that combines low-latency durable appends with low-cost archival for the common single-writer architecture. BtrLog replicates log records across a quorum of SSD-backed log nodes in a single network round trip, reducing sensitivity to stragglers in commit latency. To minimize storage cost, log nodes archive records to object storage as large segments, which are written asynchronously and off the latency-critical write path. In our evaluation, BtrLog achieves lower latency than EBS and enables higher end-to-end transaction throughput when integrated into a DBMS…More than half of adults in U.S. say they lack basic statistical understanding
In the study, published in PLOS One, the researchers analyzed nationally representative survey data and found that 62% of respondents reported no or limited statistical knowledge while only 11% regularly use statistics in daily life. Despite the perceived knowledge gap, most respondents also reported they would rely on statistics for decision-making more if they understood the topic better…Academic Alpha - A (Re)Introduction to Style Premia Investing
In this paper, we revisit the concept and benefits of style premia investing through the lens of “academic alpha”—sources of return grounded in well-established academic research. We discuss why identifying robust and implementable factors within the so-called “factor zoo” requires substantial manager skill, and how practitioners can improve and expand upon academic ideas through better signal measurement, broader asset-class applications, portfolio construction and risk management.RAG Is Simpler Than You Think
Six approaches to retrieval-based AI, from minimal to elaborate…Nowadays, most people seem to over-engineer their RAG stack. They jump straight to embeddings, vector databases, and reranking pipelines. Meanwhile, their users just want to find the doc that says “How to reset my password.”…Before we dive into recipes, let’s establish when you should use each approach. The key factors are: 1) Data Freshness Requirements, 2) Corpus Characteristics, 3) Query Patterns, 4) Scale & Performance, 5) and Team Capabilities…Coming soon
There’s a pizza shop near my home with a sign out front that says “Coming Soon.” When I drove by it this morning I thought about how you would model the time until an event happens that is “coming soon.”…Suppose I look at the sign one day and guess how many days until the pizza shop will open. When I drive by a week later and guess again, should my guess be smaller? You might argue that the shop will open some day, fixed in time but unknown to me, and so every day I’m one day closer to the eventual opening…Running local large language models: not as difficult as you might think
This tutorial walks through the setup…What you’ll need:
A desktop or laptop computer, more on hardware below.
Download the Ollama software
A large language model, it’s simple to use Ollama to download one.
Ideally, another piece of software that interacts with Ollama — this can either be, say, ellmer, the R package, which can make calls to Ollama, or it can be an extension like Continue, which lets you do agentic coding and autocomplete…
Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart
Scaling Marketplace experiments requires specialized statistical techniques. We examine why standard ordinary least squares regression (OLS) becomes computationally intractable when controlling for high-cardinality categories. We then dive into the underlying math and demonstrate how modern packages — specifically Fixest and Pyfixest — bypass these limitations. We conclude by benchmarking these methods to show their real-world impact on processing speed, memory efficiency, and estimator precision…Problems with large tables in Postgres
Tell me to stop when I name the largest table in your database: logs, ledger, notifications, feed, events, chonk ... admit it, I got it, right? Product success leads to lots of data, lots of data leads to big tables, but big tables lead to predictable problems. A table can be large (many rows), wide (many columns), or “fat” (oversized values). Any of these can cause you problems. How a large table can cause an outage First, let me tell you a story…
How do you test ETL pipelines? [Reddit]
I’m interested in learning how others test ETL pipelines in real-world projects.How do you validate source-to-target data, transformations, duplicates, missing records, incremental loads, and data quality?…Also, how do you handle ETL testing when the data volume is very large or when the transformation logic changes?…
Policy Gradients Part 1: The REINFORCE Estimator
I have a dirty secret. Well, I actually have many. But one of them is that I never understood the basic algorithms behind reinforcement learning. So I plan to remedy this with a series of blog posts, where I will cover foundational RL algorithms, from REINFORCE to the frontier. Here’s the first one, where I cover one of the oldest practical gradient estimators for reinforcement learning, the REINFORCE estimator, and show how it can get away without differentiating through the environment…
What’s the most counterintuitive statistical fact that’s actually true?
What statistical concepts are commonly misunderstood by the general public?
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* Based on unique clicks.
** You can find last week's issue #665 here.
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Thank you for joining us this week! :)
Stay Data Science-y!
All our best,
Hannah & Sebastian