TryDataPractical guides on data engineering, AI automation, and cloud infrastructure, written by engineers who build this stuff daily.
Most ETL pipelines break not because of bad code, but bad design decisions. We share the 5 practices that separate reliable data infrastructure from pipelines that fail at 3am.
Confused between data warehouse and data lake? This practical guide explains the real difference, when to use each, and the right architecture for your current business stage.
AI automation ROI is very measurable if you use the right framework. Includes a step-by-step calculation, worked examples, and red flags that mean automation will not pay off.
We publish practical guides every two weeks. Topics coming up: RAG system implementation, Kubernetes for data teams, dbt best practices.