If you use a feature store to store your DataFrames (most provide APIs for storing Polars, Pandas, PySpark DataFrames in backing Lakehouse/real-time DBs), then you get type checks when writing data to the DataFrame's backing Feature Group (Lakehouse + real-time tables).
Many also add an additional layer of data validation on top of schema validation, using frameworks like Great Expectations. For example, it's not enough to know 'age' is an Integer, it should be an integer in the range 0..150.
Comments
If you use a feature store to store your DataFrames (most provide APIs for storing Polars, Pandas, PySpark DataFrames in backing Lakehouse/real-time DBs), then you get type checks when writing data to the DataFrame's backing Feature Group (Lakehouse + real-time tables).
Many also add an additional layer of data validation on top of schema validation, using frameworks like Great Expectations. For example, it's not enough to know 'age' is an Integer, it should be an integer in the range 0..150.
Disclaimer: i work for Hopsworks.