diff --git a/Getting_Started/Part_2_Reading_Spatial_Files.ipynb b/Getting_Started/Part_2_Reading_Spatial_Files.ipynb
index 80b70d3..b68353f 100644
--- a/Getting_Started/Part_2_Reading_Spatial_Files.ipynb
+++ b/Getting_Started/Part_2_Reading_Spatial_Files.ipynb
@@ -92,7 +92,6 @@
     "\n",
     "- GeoParquet from an S3 bucket\n",
     "- GeoJSON from the notebook's local file storage\n",
-    "- Shapefile\n",
     "- A CSV file with latitude and longitude stored in two columns\n",
     "\n",
     "In all these examples, we are loading the data into an Apache Spark DataFrame."
@@ -175,15 +174,13 @@
    "source": [
     "Let's break those calls down.\n",
     "\n",
-    "**GeoParquet**: The Wherobots [Spatial Catalog](https://cloud.wherobots.com/spatial-catalog) hosts datasets stored in S3 buckets. \n",
+    "**GeoParquet**: The Wherobots [Data Hub](https://cloud.wherobots.com/data-hub) hosts datasets stored in S3 buckets. \n",
     "\n",
     "- `format(\"geoparquet\")` → Specifies that we are reading a GeoParquet file.\n",
     "- `load(\"s3a://...\")` → Loads the dataset directly from S3 without downloading it locally.\n",
     "\n",
     "**GeoJSON** is often used for web-based mapping applications. GeoJSON data is often hierarchical, so it's often useful to pull those fields from inside a struct and make them columns of their own.\n",
     "\n",
-    "**Shapefiles** consist of multiple files (`.shp`, `.dbf`, `.shx`), so we load the directory containing them.\n",
-    "\n",
     "**CSV** cannot store binary fields like geometries, so spatial data often needs to be converted so we can use WherobotsDB's spatial query functions.\n",
     "\n",
     "- `option(\"header\", \"true\")` → Reads the first line as column names.\n",