Real-world data is almost always messy. And as a data scientist or analyst, you need to discover the facts about the data. To do so, the data must be tidy and free from errors. Hence, the very first step is to clean the data. Therefore, I... Read more

When it comes to feature stores, there are two main approaches to feature engineering. One approach is to build a domain-specific language (DSL) that covers all the possible feature engineering steps (e.g., aggregations, dimensionality reduction, and transformations) that a data scientist might need. The second approach... Read more

Feature stores began in the world of Big Data, with Spark being the feature engineering platform for Michelangelo (the first feature store) and Hopsworks (the first open-source feature store). Nowadays, the modern data stack has assumed the role of Spark for feature stores – feature engineering... Read more

Python has grown to become one of the most popular and well-liked programming languages in the world, used by millions of developers since its creation in 1991. For data scientists in particular, Python has a strong, long-time base of developers. Why is Python the language of... Read more