Writing
What to learn in AI/ML, in order: Python, SQL and useful maths, then classic ML, deep learning, RAG and agents, and cloud MLOps, with a project per stage.
Discover what the Data Lakehouse architecture is, why it's replacing traditional data warehouses and data lakes, and how you can build one using Apache Iceberg, Delta Lake, and…
A data engineer's weekly reflection: automating a manual ingestion job, asking for help sooner, Airflow dynamic task mapping, and collaboration over heroics.
What linear regression is, why it matters for businesses, real-world applications, how it works step by step, key evaluation metrics, limitations, and best practices.
Discover the 5 essential AI and ML tools data engineers need in 2026 — from dbt LLM macros to MLflow, Feast, Great Expectations, and cloud ML pipelines.
Every data engineer has a story. It usually starts the same way: someone needed a quick data pull, so you wrote a Python script. It worked. Then it…
A senior data engineer reflects on one week of building, writing, and teaching — the lessons that actually moved the needle, and the ones you can skip.
From medical imaging to ICU deterioration models, AI is quietly transforming healthcare in 2026. Here's what's actually in production — and why none of it works without serious…
A data engineer's honest take on Polars vs Pandas in 2026: what Polars does differently, real benchmarks, migration tips, and when pandas is still the right call.