The Road To Data Mesh: BlaBlaCar’s 3-year Journey To Self-serve Data Analytics
summary
BlaBlaCar spends three years moving from a central warehouse to a domain-oriented data mesh, pairing domain teams with a foundations team and using Monte Carlo for end-to-end observability; lineage helps untangle 10,000 tables, key-asset scoring drops thousands of unused tables, a marketing POC runs for three months and accelerates delivery, Monte Carlo installs in a two-week alerting setup, and investigation time falls from 200 hours a quarter toward near zero while ownership, contracts and SLOs remain works in progress.
AI-written summary · about
chapters (15)
- 0:00Introduction and welcome
- 1:05About BlaBlaCar and roles
- 4:16Data’s role at BlaBlaCar
- 6:22Why data mesh at BlaBlaCar
- 8:33Tooling and governance in data mesh
- 11:00Data observability and quality challenges
- 14:05Discovering Monte Carlo
- 15:33BlaBlaCar’s data stack
- 17:41Monte Carlo’s lineage and key assets
- 19:48Measuring data quality improvements
- 21:12Data mesh unlocking new products
- 22:57Mesh teams and loan expert syndrome
- 24:01Audience Q&A on data quality
- 25:50Data quality ownership per domain
- 28:19Progress on data mesh principles
related talks
44mData Mesh: Lessons from the trenches
38mLesson learned implementing a large-scale data mesh transformation
48mBuilding a data mesh
57mOur DataMesh Journey: data architectures for microservices
01h 02mShift Data Quality Left with Data Mesh Principles
44mData Mesh
50mAmy Raygada | Data Track | Compass AI & Data Summit 2025
39mData Mesh: Why, What and How