Case Study: BairesDev improves data quality and trust with Monte Carlo and Databricks

Migrating to Data Mesh at BairesDev with Databricks and Monte Carlo

BairesDev, a rapidly growing software solutions company with a distributed global team, faced significant data management challenges including a lack of cohesion in technology, widespread distrust in data quality, and performance issues that hindered scalability. To address these problems, they partnered with Monte Carlo to implement a data observability platform as a core component of their new data mesh architecture.

Monte Carlo provided the data observability solution that automated data quality standards and federated governance across BairesDev's decentralized domains. This allowed domain teams to manage incidents and receive targeted alerts without sacrificing their autonomy. The implementation, which also included other managed tools, resulted in improved data availability, rebuilt trust in data, and enabled the company to successfully scale its analytical workloads.

BairesDev

Matheus Espanhol

Data Engineering Manager