Success stories
Learn how dbt fuels sensemaking for organizations of all sizes.

“Our evergreen platform, built on dbt and Databricks, lets our teams around the world work from the same data sets. That translates to higher productivity and greater trust in our data as we work to become a global enterprise.”Nana Essuman, Senior Director of Data Engineering & Data Warehouse at Condé Nast

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80% Reduction in data engineering hours, reducing costs
95% Reduction in time to onboard new employees
6-Figure annual savings in IT headcount costs


3 months to build a usable prototype
5 months shaved off the product development timeline
1 new approach to product development


+100 data team NPS, rated by internal stakeholders
1 week to build new dashboards, down from months
2 hours to debug, down from half a day


3-month migration of 26 data sources, 1200 models, and 6300 tests to dbt
$0 increase in total cost of ownership
6-8 hour data infrastructure maintenance windows reduced to 0


€1.3bn in sales tracked across restaurants, drive-throughs, and delivery
4 markets with different data stacks centralized
5x faster delivery times for historical data


2x increase in the number of people collaborating on data modeling
3 weeks of work eliminated from regulatory reporting
$10M reallocated back into the business


20% reduction in data platform costs
33% increase in data transformation speed
25% of all sales pipeline generated by their new personalization engine


100 hours saved on a typical data integration
80% less time spent on data processing jobs
30% more clients supported without increasing IT head count


600% decrease in time to actionable data
8x increase in engineering contribution
$110,500 saved in annual engineering costs


10 engineers and 40 analysts share 1 development framework
50% reduction in engineering tickets for data issues
75% acceleration in time to deployment


500x more rides in 4 years
6 to 60 people on the data team in under 2 years
1 hour to onboard new data team members to dbt


6 months to implement and launch a new data stack
80% of work to create new data products can be self-served by analytics team
1 day to trace the root cause of issues, down from two weeks


4-8x increase in speed from idea to production
1 week for new analytics engineer hires to start shipping
10x decrease in maintenance costs


5 analysts trained with no previous data engineering experience
5x more models developed
50% reduction in model build time
