All stacks · Serving & BI
Looker, Tableau, Power BI, Superset, and the metrics layer.
BI & semantic layerQ2 revenue was $14.2M, $13.8M, and $15.1M depending on the workbook. LookML, MetricFlow, or a Power BI dataset can hold the metric — they do not replace dbt or hide extract lag.
Microsoft FabricOneLake shortcuts are not a copy job, capacity units are shared, and a Power BI refresh can queue a Spark notebook. Fabric is not rebranded Synapse.
Looker, Tableau, Power BI, Superset, and a semantic / metrics layer are how the warehouse earns its keep. The DE job is grain, certified datasets, and not letting every analyst SELECT * a 12-billion-row fact.
LookML, DAX, and published datasets are contracts. If the metric lives in three workbooks, you do not have a metric — you have an argument.
The one your org already pays for, unless it cannot do governed metrics. Looker if you want a Git-shaped semantic layer. Power BI if you are Microsoft/Fabric. Tableau if visual analysis is the culture. Superset if you want open-source and will staff it.
No. dbt builds tested tables. The semantic layer defines metrics on top. Both, or you will redefine revenue in YAML and SQL forever.
Extract lag, a filter on the wrong grain, or a join in the workbook. Replicate the number in the warehouse first. If it matches, the BI layer is the bug.
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