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 […]
Stopping rules are what make most frequentist (a.k.a. classical) statistical tests valid. Unsurprisingly, they tell you when to stop an experiment, and equally unsurprising, they are rules that must be followed in order to get statistically valid experiments from statistical tests that depend on them. However, despite stopping rules being critical to the interpretation of […]
In the era of big data, the choices of data formats are dazzling, and the concept of data format itself can be confusing. Our data platform team found it helpful to breakdown this topic based on the three major stages in the life cycle of data: in-memory representation (logical format), on-the-wire serialization (exchange format), and […]
“Before I do anything, I ask myself, would an idiot do that? And if the answer is yes, I do not do that thing.” – Dwight Schrute There are competing schools of thought about how best to approach job interviews. At Credit Karma, our broadest hiring goal is to hire great people. To do this, we need […]
The gap between development and production environments https://github.com/creditkarma/maven-exec-jar-plugin The Maven executable jar plugin was made at Credit Karma by developers who wanted their local environment to match their application’s production environment. In other deployment formats used by Java/Scala apps, jars are smashed together into either one “uber” jar or zipped into a file that must be uncompressed […]
