Micky Csasznik-Shaked Technology Lead at Mint Bills
“With Apache Cassandra and DataStax Enterprise as our database infrastructure, we can not only scale but give our users a real-time, engaging customer experience.”
Micky Csasznik-Shaked Technology Lead at Mint Bills

Mint Bills, previously Check, is a top-rated, award-winning mobile app that takes the work and worrying out of paying

bills for more than 11 million U.S. consumers. The Mint Bills application stays on top of people’s bills and money, eliminating missed payments, overdrafts, and late fees. Launched in 2008, Mint Bills  (formerly known as Check) was one of the initial 500 apps released on the first generation iPhone. Mint Bills was acquired by Intuit Inc. for $360 million in June 2014.


In the financial services sector, it is extremely challenging to get traction against established mega leaders, and stand out as a disruptor. Mint Bills is a top mobile app focused on bill payments and personal financial management, helping users manage and track bills quickly and efficiently. With a few taps, users get an easy-to-understand overview of their bills, credit cards, bank balances, and investment accounts, all in one place. The application also sends timely bill reminders coupled with mobile payment functionality, making Mint Bills a one-stop destination for everyday personal finance.


When Mint Bills began its search, relational databases were immediately ruled out because of their inability to cost effectively scale for immense volumes of data along with performance, complexity, and latency issues. “Our first decision was not to store user account data in a relational way,” said Micky Csasznik-Shaked, technology lead at Mint Bills. “Because of the large number of data models we had, to get a full view of all information across user accounts meant that we would have to join numerous tables and make many calls to the database, thus bringing down application performance.” The other issue Mint Bills had was that with relational databases alone, it couldn’t save more than current results, which limited the ability to truly understand customer profiles.

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