The Blog
Thoughts on data engineering and architecture: the concepts, patterns, and tradeoffs behind how modern data systems are built.
What is AI Engineering?
AI engineering is emerging as a distinct discipline. Here's what it actually involves, and why it matters now.
Your Data Problem Is Not What You Think
Most founders and CEOs frame their data problems as tooling problems. They're usually not. The real issue is structural, and it starts with where your data lives and who it's built for.
Data Engineers Are Software Engineers
Data engineering has come a long way, and most people now appreciate how complex and demanding the work actually is. But the idea that it's mostly writing SQL and connecting no-code tools still lingers in some corners; and it's worth unpacking why that picture falls short.
What is a Data Model and Why Should Non-Engineers Care?
Your company is collecting more data than ever, yet decisions still feel like guesswork. The missing piece is usually not more data. It's a data model. Here's what that means, in plain English.
Stop Asking 'Kafka or Spark Streaming?' It's the Wrong Question
Kafka and Spark Streaming are often mentioned in the same breath, and they're frequently used together. But they solve fundamentally different problems, and conflating them leads to architectures that are harder to reason about than they need to be.
From Data Warehouse to Data Lake to Lakehouse: What Actually Changed and Why It Matters
A practical look at how data architecture evolved from rigid warehouses to sprawling lakes and finally to the lakehouse pattern that combines the best of both, and why the distinction matters when you're choosing where to build.
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