Original Summary

We’re finally migrating away from our legacy BI tool because our metric definitions are a complete mess (different departments have different SQL queries for the same KPIs). We are currently evaluating open-source competitor alternatives and the internal debate has basically come down to two entirely different architectural paths: Apache Superset vs Cube I know they aren't 1:1 comparisons, which is why we are stuck: Path A: Apache Superset It gives us the visualization layer and dashboards right out of the box. But my concern is that we are just shifting our problem to a new tool. The metric definitions still live inside Superset's dataset layer. If we ever want to feed those same metrics into a custom React app or another tool, we are stuck. Path B: Cube dev. We build a standalone context layer (semantic layer) on top of our data warehouse. Cube handles the metrics, access control, and caching (pre-aggregations). The massive upside is that our logic is completely decoupled and accessible via API. The downside? We have to bring our own visualization tool or build the front-end ourselves. For data teams that have faced this fork in the road: did you choose the out-of-the-box convenience of Superset, or did you invest in building a standalone context layer with Cube? Can they realistically be paired together without over-engineering?   submitted by   /u/Eastern_Eye_8637 [link]   [comments]

  • 情报分类：商业与市场研究
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  • 信息来源：Reddit · SaaS
  • 发布时间：2026/9/14 23:14:59