dbt Labs × AXS
dbt Labs x AXS, 50% faster deploys for global ticketing platform
50% faster to deploy new models
Manual, error-prone workflows to automated analytics engineering
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Why it works
The case study leads with three concrete efficiency numbers (50% faster deploys, 10% faster troubleshooting, 40% saved maintenance hours), giving readers an immediate quantified payoff. The before-state is clearly described as manual ETL, poor observability, and inconsistent metrics, which grounds the numbers in a real problem. The quote from Michael Colella carries technical detail rather than filler, explaining the SQL-first approach and its effect on team collaboration.
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Pairing each headline metric with the specific feature that drove it (incremental models for deploy speed, lineage graphs for troubleshooting) makes the mechanism transparent and replicable, not just a raw number.
Click to enlarge ↗ This is editorial commentary and curation. The case study, screenshot, and all metrics are dbt Labs's published work; we link to the source and lead with our analysis.