What the Data Crowd Was Reading in March 2026

Tools, techniques and deep dives worth reading that I came across in March 2026.

Fellow Data Tinkerers

It’s time for another round-up on all things data and AI!

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Without further ado, let’s get to the round up for March!

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Data science & AI

• State of Context Engineering in 2026 (12 minute read)
  Aurimas Griciūnas argues that context engineering is evolving from prompt tinkering into a structured discipline where managing memory, retrieval and state becomes the core challenge of building reliable AI systems.

  

• Bayesian statistics for confused data scientists (15 minute read)
  Nicolas Chagnet explains Bayesian statistics in plain terms by showing that its real strength is not mathematical elegance but giving data scientists a cleaner way to reason about uncertainty and sparse real-world data.

• Agentic AI Engineering Guide (10 minute read)
  Paul Iusztin argues that most agentic AI systems fail not because the model is weak, but because teams make avoidable engineering mistakes around context, architecture, planning and evaluation.

  

• The Context Problem (29 minute read)
  Jessica Talisman argues that the AI industry has turned context into a token-priced billing unit even though context should really mean the relational structure that makes information coherent and useful.

• A Visual Guide to Attention Variants in Modern LLMs (26 minute read)
  Sebastian Raschka, PhD shows how modern LLM attention has evolved from standard multi-head attention into a growing set of variants each designed to balance quality, memory use and inference efficiency.

  

• The Boon of Dimensionality (6 minute read)
  Amandeep Singh shows that high-dimensional space creates the geometric conditions that make embeddings, random projections and feature separation work in modern machine learning.

• GPT 5.4 is a big step for Codex (9 minute read)
  Nathan Lambert writes that GPT 5.4 feels like a real step forward for Codex, with gains in usability, speed, context handling and agent reliability that matter more in practice than benchmark scores alone.

• How Shopify Scales Taxonomy Evolution Across 10,000+ Categories With Multi-Agent AI (14 minute read)
  This piece breaks down how Shopify moved from reactive manual updates to a multi-agent system that scans taxonomy branches in parallel, proposes new categories/attributes from merchant data, detects duplicates and runs automated QA through domain-specific judges.

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Data engineering

• Layer by Layer, We Built Data Systems No One Understands (9 minute read)
  SeattleDataGuy writes that modern data stacks keep piling on layers in the name of simplicity but the result is often more sprawl, more cost and systems that are harder to understand or tie back to business outcomes.

  

• ETL is Dead (12 minute read)
  Ananth Packkildurai argues that ETL is not disappearing in volume but it is fading as the core identity of data engineering as AI shifts the real work toward context and semantics.

• The Data Engineer’s GitHub Portfolio (2026 Edition) (10 minute read)
  Yordan Ivanov writes that a strong data engineering GitHub portfolio should prove technical taste, system thinking and real-world problem solving, not just show a pile of tutorial projects.

  

• The Data Engineering Mindset Every AI Builder Needs (14 minute read)
  Erfan Hesami writes that most AI products do not break because of the model but because builders ignore the data foundations early on, especially data flow design, data quality and monitoring.

• The absolute beginners guide to databasemaxxing (18 minute read)
  This article walks through database internals from a beginner’s perspective, showing how concepts like parsing, binding, scans and index seeks fit together under the hood.

• Your Data Model Isn’t Broken, Part I: Why Refactoring Beats Rebuilding (12 minute read)
  Chris Hillman makes the case that most broken data models are really bundles of hard-won business knowledge and that careful refactoring is usually smarter than blowing everything up and starting again.

• ClickHouse -> Real-time insight in 15 minutes (19 minute read)
  Phi Vu Trinh shows that ClickHouse is built for real-time analytics but getting that performance in production usually means handling enough operational complexity that makes platforms like Tinybird appealing.

  

• How Notion Scaled AI Q&A to Millions of Workspaces (14 minute read)
  This article walks through how Notion scaled AI Q&A to millions of workspaces while increasing onboarding throughput 600x and cutting costs by up to 90%.

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Other interesting reads

• Relevance of Business Models for Data (10 minute read)
  Dylan Anderson makes the case that data teams should start with the business model first, because strategy, architecture, governance and analytics all work better when they are tied to how the company actually creates and captures value.

  

• It’s about the strategy, stupid (14 minute read)
  Timo Dechau makes the case that data work becomes far more useful when it starts with business strategy, not with dashboards, tracking audits or whatever tactic happens to be fashionable.

• What do frontier AI companies’ job postings reveal about their plans? (9 minute read)
  Interesting article suggesting that frontier labs’ job postings reveal where the market is heading, with hiring patterns pointing to heavier go-to-market pushes, new product bets and different strategies for securing compute and data.

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Quick favor - need your take

Was there any standout article or topic from March I missed? Feel free to drop a comment or hit reply, even a quick line helps.

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