Disrupting the financial services industry
I didn't find a better option than Datomic with this first class concept of time. For me, that makes all of the difference.
Brazil’s banking sector is dominated by five institutions controlling over 90 percent of national assets, offering nearly identical products at similar price points. The country struggles with some of the world’s highest credit card interest rates. Nubank’s founders established their fintech startup with a mission to disrupt this landscape through technology-driven differentiation from inception.
Challenge
Legacy technology couldn’t meet Nubank’s requirements. The company needed infrastructure flexible enough to balance technical operations with analytical work — running complex queries against live data without disrupting customer-facing systems, investigating bugs, and addressing intricate business problems.
Answer: Datomic
Nubank selected Datomic as its data infrastructure foundation, enabling the organization to build a system designed for regulatory compliance, complexity, and auditing while maintaining startup agility.
Foundation for Growth
Datomic delivered transactional performance for customer operations alongside horizontal read scalability for analytics and machine learning without traditional ETL pipelines. The platform’s built-in temporal capabilities proved invaluable during service splits and database transitions, supporting transaction replay and historical correction. Multiple storage backends accommodated encryption requirements for personally identifying information. Datalog’s recursive capabilities elegantly solved specialized problems, like mapping customer network evolution over time.
Business Benefits
Customers benefit directly — Nubank demonstrates complete transaction histories and investigates service attacks through Datomic’s audit trail. When encountering corrupted or incorrect data from external sources, the system’s historical recovery capabilities provide essential protection.
The Spark Connection
Nubank deployed Apache Spark clusters to parallelize expensive analytical queries across multiple Datomic databases, enabling horizontal scaling as data volumes grow while isolating analytical workloads from production systems.