diff --git a/docs/source/dataframe_comparison.rst b/docs/source/dataframe_comparison.rst
index 609390dfbe..fb76883a42 100644
--- a/docs/source/dataframe_comparison.rst
+++ b/docs/source/dataframe_comparison.rst
@@ -50,6 +50,6 @@ Ray Datasets
 
 Ray Datasets make it easy to feed data really efficiently into Ray's model training and inference ecosystem. Datasets also provide basic functionality for data preprocessing such as mapping a function over each data item, filtering data etc.
 
-However, Ray Datasets are not a fully-fledged Dataframe abstraction (and `it is explicit in not being an ETL framework for data science <https://docs.ray.io/en/latest/data/faq.html#what-should-i-not-use-ray-datasets-for>`_) which means that it lacks key features in data querying, visualization and aggregations.
+However, Ray Datasets are not a fully-fledged Dataframe abstraction (and `it is explicit in not being an ETL framework for data science <https://docs.ray.io/en/latest/data/overview.html#ray-data-overview>`_) which means that it lacks key features in data querying, visualization and aggregations.
 
 Instead, Ray Data is a perfect destination for processed data from DaFt Dataframes to be sent to with a simple :meth:`df.to_ray_dataset() <daft.DataFrame.to_ray_dataset>` call. This is useful as an entrypoint into your model training and inference ecosystem!