data-engineering
4 free lessons tagged data-engineering across AI, Programming. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Features, Feature Stores, and Training-Serving Skew
The most common way a good model fails in production is that the features it is served differ from the ones it was trained on. This lesson covers where that divergence comes from, why point-in-time correctness is harder than it looks, what a feature store actually solves, and when you do not need one.
Why ML Systems Rot
A trained model is a small box inside a large system, and the system is what decays. This lesson covers the failure modes that are specific to machine learning: why changing one feature moves every weight, why hidden consumers break silently, and why data dependencies cost more than code dependencies.
Apache Airflow: Orchestrating Data Pipelines
Dive into Apache Airflow, the powerful platform for programmatically authoring, scheduling, and monitoring complex data workflows. Learn about DAGs, operators, and how to build robust, scalable pipelines for modern data engineering.
Understanding the Data Lakehouse Architecture
Explore the data lakehouse, a modern architecture that merges the cost-efficiency and flexibility of data lakes with the performance and reliability of data warehouses. This lesson covers its core components, benefits, and the open-source technologies that make it possible.