QuickCheck, a Fintech startup based in Lagos, Nigeria, is on a mission to provide financial services to over 60 million Nigerian adults who are excluded from banking services and 100 million who do not have access to credit. The QuickCheck mobile app, which has been downloaded by more than 2 million people and has processed over 4.5 million micro-credit applications, leverages artificial intelligence to offer app-based neo-banking products. However, the company faced challenges in analyzing the vast amount of financial data, fraud analysis, and monitoring data. They needed a solution that could handle hundreds of thousands of rows of data loaded daily for portfolio risk analysis and financial metrics building.

TrillaBit, a dynamic SaaS platform for reporting and business intelligence, initially used Apache Solr as its data backend. However, they soon encountered several challenges. Solr, being a key-value store, was more suited to search than high-volume non-linear aggregation or data compression for performance. Its query language wasn’t as mature as SQL and it didn’t handle joins effectively. When implementing real company data from various sources, TrillaBit found that more flexibility was required in different scenarios. They needed a solution that could be managed at a low cost and could be implemented within their environment for hands-on experience and understanding. However, popular contenders like Snowflake were too expensive and didn’t allow for full on-prem implementation.