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 code can be written that seamlessly scales to large data volumes in Snowflake, BigQuery, or Redshift. However, Python developers know that feature engineering is so much more than the aggregations and data validation you can do in SQL and DBT. Dimensionality reduction, whether using PCA or Embeddings, and transformations are fundamental steps in feature engineering that are not available in SQL, even with UDFs (user-defined functions), today. Over the last few years, we have had an increasing number of customers who prefer working with Python for feature engineering.

Hopsworks, the company that created the first Enterprise Feature Store for machine learning, released Hopsworks 3.0. This evolution brings a new offering that removes the gap between Python prototypes and production projects, resulting in the first Python-centric Feature Store for machine learning. Data Scientists' language of choice is Python, which is the dominant language for creating feature and training pipelines. However, there are challenges when piping enterprise data into machine learning models reliably and at scale. This risks delaying or derailing machine learning initiatives before they're productive.By bridging the gap between the Data Science friendly Python environments and an enterprise's data, Hopsworks dramatically enhances developer productivity by keeping the focus on building pipelines and getting machine learning models into production, and keeping them there.