Most BI tool comparisons are written by someone who spent a weekend with each option, deployed a toy dataset, and wrote up their impressions. This is not that.
Over the past three months I shipped 12 dashboards across 5 tools: Streamlit, Plotly Dash, Apache Superset, Evidence.dev, and Grafana. Each one was the visualization layer on a real data pipeline with Airflow, DuckDB, dbt, and live API data. I ran into real failures, real deployment constraints, and real differences in where each tool fits -- and where it does not.
This is that article.
The Setup
Every project I built follows the same general pattern: an Airflow 3.0 pipeline pulls data from somewhere, dbt transforms it into mart tables, and a visualization layer sits on top. The question was always: what goes in that last layer?
