We use big data at Credit Karma for data science modeling and analytical reporting, which helps us assess the efficacy of our products and services. We process and store data in Google BigQuery at scale, and we do this with data security, data quality, data lineage and data management as our key requirements. These make […]
Tech Talks: Building Competing Models Using Apache Spark DataFrames from Credit Karma on Vimeo. In this video, Abdulla Al-Qawasmeh, Engineering Manager, talks about the important role model calibration plays in the performance of Credit Karma’s recommendation system, and how to build predictive recommendation models using Apache Spark DataFrames.
Credit Karma leverages data for over 60 million members to deliver a personalized user experience. When you visit our site or use our mobile apps, we crunch your credit report and other factors in real time to understand how we can help you improve your credit score, refinance your credit card, or otherwise make financial […]
When I started at Credit Karma in late 2014, we were introducing Kafka into our data architecture. We were building out what would become the core data infrastructure for the company, which is and was growing rapidly. In a mere three months we were pushing 175k JSON events per minute into Kafka. At that time, […]