diff --git a/.gitignore b/.gitignore
index 02e651c..1368305 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,3 +5,7 @@ dist
 stac_geoparquet/_version.py
 .cache
 site
+docs/**/*.jsonl
+docs/**/*table
+docs/**/*.parquet
+.ipynb_checkpoints
diff --git a/docs/examples/naip.ipynb b/docs/examples/naip.ipynb
new file mode 100644
index 0000000..e452da3
--- /dev/null
+++ b/docs/examples/naip.ipynb
@@ -0,0 +1,455 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NAIP example\n",
+    "\n",
+    "We'll use STAC Items from the [NAIP STAC Collection](https://planetarycomputer.microsoft.com/dataset/naip) on Microsoft's Planetary Computer to illustrate how to use the `stac-geoparquet` library.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a few libraries we need to install to run this notebook:\n",
+    "\n",
+    "```\n",
+    "pip install planetary-computer pystac-client stac-geoparquet pyarrow\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import planetary_computer\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.parquet as pq\n",
+    "import pystac_client\n",
+    "import deltalake\n",
+    "import stac_geoparquet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can open the Planetary Computer STAC Collection with `pystac_client.Client.open`, ensuring we also sign the returned URLs in each STAC Item.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "catalog = pystac_client.Client.open(\n",
+    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
+    "    modifier=planetary_computer.sign_inplace,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll access the NAIP collection from the Planetary Computer catalog and download 1000 items from this collection, writing them to a newline-delimited JSON file in the current directory.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "items_iter = catalog.get_collection(\"naip\").get_items()\n",
+    "\n",
+    "max_items = 1000\n",
+    "naip_json_path = Path(\"naip.jsonl\")\n",
+    "if not naip_json_path.exists():\n",
+    "    with open(naip_json_path, \"w\") as f:\n",
+    "        count = 0\n",
+    "\n",
+    "        for item in items_iter:\n",
+    "            json.dump(item.to_dict(), f, separators=(\",\", \":\"))\n",
+    "            f.write(\"\\n\")\n",
+    "\n",
+    "            count += 1\n",
+    "            if count >= max_items:\n",
+    "                break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now use `stac-geoparquet` APIs on this data.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading to Arrow\n",
+    "\n",
+    "We can load to an Arrow `RecordBatchReader` by using the `parse_stac_ndjson_to_arrow` function.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "record_batch_reader = stac_geoparquet.arrow.parse_stac_ndjson_to_arrow(naip_json_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Arrow `RecordBatchReader` represents a _stream_ of Arrow batches, which can be useful when converting a very large STAC collection, which you don't want to materialize in memory at once.\n",
+    "\n",
+    "We can convert this to an Arrow table with `read_all`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "assets: struct<image: struct<eo:bands: list<item: struct<common_name: string, description: string, name: string>>, href: string, roles: list<item: string>, title: string, type: string>, rendered_preview: struct<href: string, rel: string, roles: list<item: string>, title: string, type: string>, thumbnail: struct<href: string, roles: list<item: string>, title: string, type: string>, tilejson: struct<href: string, roles: list<item: string>, title: string, type: string>>\n",
+       "  child 0, image: struct<eo:bands: list<item: struct<common_name: string, description: string, name: string>>, href: string, roles: list<item: string>, title: string, type: string>\n",
+       "      child 0, eo:bands: list<item: struct<common_name: string, description: string, name: string>>\n",
+       "          child 0, item: struct<common_name: string, description: string, name: string>\n",
+       "              child 0, common_name: string\n",
+       "              child 1, description: string\n",
+       "              child 2, name: string\n",
+       "      child 1, href: string\n",
+       "      child 2, roles: list<item: string>\n",
+       "          child 0, item: string\n",
+       "      child 3, title: string\n",
+       "      child 4, type: string\n",
+       "  child 1, rendered_preview: struct<href: string, rel: string, roles: list<item: string>, title: string, type: string>\n",
+       "      child 0, href: string\n",
+       "      child 1, rel: string\n",
+       "      child 2, roles: list<item: string>\n",
+       "          child 0, item: string\n",
+       "      child 3, title: string\n",
+       "      child 4, type: string\n",
+       "  child 2, thumbnail: struct<href: string, roles: list<item: string>, title: string, type: string>\n",
+       "      child 0, href: string\n",
+       "      child 1, roles: list<item: string>\n",
+       "          child 0, item: string\n",
+       "      child 2, title: string\n",
+       "      child 3, type: string\n",
+       "  child 3, tilejson: struct<href: string, roles: list<item: string>, title: string, type: string>\n",
+       "      child 0, href: string\n",
+       "      child 1, roles: list<item: string>\n",
+       "          child 0, item: string\n",
+       "      child 2, title: string\n",
+       "      child 3, type: string\n",
+       "bbox: struct<xmin: double, ymin: double, xmax: double, ymax: double>\n",
+       "  child 0, xmin: double\n",
+       "  child 1, ymin: double\n",
+       "  child 2, xmax: double\n",
+       "  child 3, ymax: double\n",
+       "collection: string\n",
+       "geometry: binary\n",
+       "  -- field metadata --\n",
+       "  ARROW:extension:name: 'geoarrow.wkb'\n",
+       "  ARROW:extension:metadata: '{\"crs\":{\"$schema\":\"https://proj.org/schemas/' + 1296\n",
+       "id: string\n",
+       "links: list<item: struct<href: string, rel: string, title: string, type: string>>\n",
+       "  child 0, item: struct<href: string, rel: string, title: string, type: string>\n",
+       "      child 0, href: string\n",
+       "      child 1, rel: string\n",
+       "      child 2, title: string\n",
+       "      child 3, type: string\n",
+       "stac_extensions: list<item: string>\n",
+       "  child 0, item: string\n",
+       "stac_version: string\n",
+       "type: string\n",
+       "datetime: timestamp[us, tz=UTC]\n",
+       "gsd: double\n",
+       "naip:state: string\n",
+       "naip:year: string\n",
+       "proj:bbox: list<item: double>\n",
+       "  child 0, item: double\n",
+       "proj:centroid: struct<lat: double, lon: double>\n",
+       "  child 0, lat: double\n",
+       "  child 1, lon: double\n",
+       "proj:epsg: int64\n",
+       "proj:shape: list<item: int64>\n",
+       "  child 0, item: int64\n",
+       "proj:transform: list<item: double>\n",
+       "  child 0, item: double\n",
+       "providers: list<item: struct<name: string, roles: list<item: string>, url: string>>\n",
+       "  child 0, item: struct<name: string, roles: list<item: string>, url: string>\n",
+       "      child 0, name: string\n",
+       "      child 1, roles: list<item: string>\n",
+       "          child 0, item: string\n",
+       "      child 2, url: string"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = record_batch_reader.read_all()\n",
+    "table.schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also pass a small chunk size into `parse_stac_ndjson_to_arrow` to show how the streaming works.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "record_batch_reader = stac_geoparquet.arrow.parse_stac_ndjson_to_arrow(\n",
+    "    naip_json_path, chunk_size=100\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`record_batch_reader` is an iterator that yields Arrow `RecordBatch` objects. If we load just the first one, we'll see that it contains 100 rows.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_batch = next(record_batch_reader)\n",
+    "first_batch.num_rows"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Materializing the rest of the batches from the iterator into a table gives us the other 900 rows in the dataset.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "900"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "other_batches = record_batch_reader.read_all()\n",
+    "other_batches.num_rows"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All batches from the RecordBatchReader have the same schema, so we can concatenate them back into a single table:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined_table = pa.concat_tables([pa.Table.from_batches([first_batch]), other_batches])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both the original `table` object and this `combined_table` object have the exact same data:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table == combined_table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Converting to Parquet\n",
+    "\n",
+    "We can use the utility function `parse_stac_ndjson_to_parquet` to convert the items directly to GeoParquet.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "naip_parquet_path = \"naip.parquet\"\n",
+    "stac_geoparquet.arrow.parse_stac_ndjson_to_parquet(naip_json_path, naip_parquet_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reading that Parquet data back into Arrow with `pyarrow.parquet.read_table` gives us the exact same Arrow data as before.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pq.read_table(naip_parquet_path) == table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Converting to Delta Lake\n",
+    "\n",
+    "We can use the utility function `parse_stac_ndjson_to_delta_lake` to convert items directly to Delta Lake.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "naip_delta_lake_path = \"naip_table\"\n",
+    "stac_geoparquet.arrow.parse_stac_ndjson_to_delta_lake(\n",
+    "    naip_json_path, naip_delta_lake_path, mode=\"overwrite\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reading the Delta Lake table back into Arrow with `deltalake.DeltaTable` gives us the exact same Arrow data as before.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "deltalake.DeltaTable(naip_delta_lake_path).to_pyarrow_table() == table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "stac-geoparquet",
+   "language": "python",
+   "name": "stac-geoparquet"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/mkdocs.yml b/mkdocs.yml
index 0d253da..d0250b6 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -18,6 +18,8 @@ nav:
   - index.md
   - usage.md
   - schema.md
+  - Examples:
+      - examples/naip.ipynb
   - Specification: spec/stac-geoparquet-spec.md
   - API Reference:
       - api/arrow.md
@@ -74,6 +76,9 @@ plugins:
   - mike:
       alias_type: "copy"
       canonical_version: "latest"
+  - mkdocs-jupyter:
+      include_source: true
+      ignore: ["**/.ipynb_checkpoints/*.ipynb"]
   - mkdocstrings:
       enable_inventory: true
       handlers:
diff --git a/pyproject.toml b/pyproject.toml
index 99ee7d5..c759a16 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,6 +36,7 @@ docs = [
     "black",
     "griffe-inherited-docstrings",
     "mike>=2",
+    "mkdocs-jupyter",
     "mkdocs-material[imaging]>=9.5",
     "mkdocs",
     "mkdocstrings[python]>=0.25.1",