diff --git a/.gitignore b/.gitignore
index d88455c..049d175 100644
--- a/.gitignore
+++ b/.gitignore
@@ -167,3 +167,4 @@ docs/generated
 
 tests/duckdb-extensions
 tests/out.json
+data/its-live*
diff --git a/docs/notebooks/its-live.ipynb b/docs/notebooks/its-live.ipynb
new file mode 100644
index 0000000..e13ae4f
--- /dev/null
+++ b/docs/notebooks/its-live.ipynb
@@ -0,0 +1,467 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "23e8ca35",
+   "metadata": {},
+   "source": [
+    "# ITS_LIVE case study\n",
+    "\n",
+    "An example of using **rustac** with [ITS_LIVE](https://its-live.jpl.nasa.gov/) STAC data.\n",
+    "We'll start just by looking at the Landsat ndjson.\n",
+    "\n",
+    "<div class=\"admonition note\">\n",
+    "    <p class=\"admonition-title\">AWS credentials</p>\n",
+    "    <p>\n",
+    "        You'll want to make sure you're running this notebook with your AWS credentials configured in your environment, and set your default region to us-west-2.\n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a8a0bf03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from obstore.store import S3Store\n",
+    "\n",
+    "destination = Path(\"../../data/its-live\")\n",
+    "source_store = S3Store(\n",
+    "    bucket=\"its-live-data\", prefix=\"test-space/stac_catalogs/landsatOLI/v02\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "812b1fe1",
+   "metadata": {},
+   "source": [
+    "Let's list all of the ndjson files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d7a7afdd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 5134 paths with sizes ranging from 2.9 kB to 233.0 MB\n",
+      "Total size of the files is 28.7 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "import humanize\n",
+    "\n",
+    "paths = []\n",
+    "sizes = []\n",
+    "for list_stream in source_store.list():\n",
+    "    for object_meta in list_stream:\n",
+    "        paths.append(object_meta[\"path\"])\n",
+    "        sizes.append(object_meta[\"size\"])\n",
+    "\n",
+    "print(\n",
+    "    f\"Found {len(paths)} paths with sizes ranging from {humanize.naturalsize(min(sizes))} to {humanize.naturalsize(max(sizes))}\"\n",
+    ")\n",
+    "print(f\"Total size of the files is {humanize.naturalsize(sum(sizes))}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7161cba",
+   "metadata": {},
+   "source": [
+    "That's a lot of data!\n",
+    "We'd like to make one or more STAC Collections from it, but we don't really want to store that much ndjson locally.\n",
+    "**stac-geoparquet** is much more compact (especially when compressed) so let's copy-and-convert.\n",
+    "\n",
+    "This will take a while, on the order of an hour or two.\n",
+    "An implementation using in-region resources would be faster.\n",
+    "\n",
+    "<div class=\"admonition note\">\n",
+    "    <p class=\"admonition-title\">Backfilling errors</p>\n",
+    "    <p>\n",
+    "        The block includes a check for already-existing files, so you can run it multiple times to pick up any files that errored.\n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1933f2c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from asyncio import TaskGroup, Semaphore\n",
+    "import tqdm\n",
+    "import rustac\n",
+    "\n",
+    "# Limit the number of files we hold in memory at a time\n",
+    "semaphore = Semaphore(10)\n",
+    "# Store the ones that error, it's the internet after all, things will error\n",
+    "missed_paths = []\n",
+    "\n",
+    "\n",
+    "async def copy_and_convert(\n",
+    "    source_path: str, source_store, destination_path: Path, progress_bar: tqdm.tqdm\n",
+    ") -> None:\n",
+    "    async with semaphore:\n",
+    "        try:\n",
+    "            value = await rustac.read(source_path, store=source_store)\n",
+    "        except Exception:\n",
+    "            missed_paths.append(path)\n",
+    "            progress_bar.update()\n",
+    "            return\n",
+    "\n",
+    "        # ndjson with only one item are read as an item, not a item collection\n",
+    "        if value[\"type\"] == \"Feature\":\n",
+    "            await rustac.write(str(destination_path), [value])\n",
+    "        else:\n",
+    "            assert value[\"type\"] == \"FeatureCollection\"\n",
+    "            await rustac.write(str(destination_path), value)\n",
+    "\n",
+    "        progress_bar.update()\n",
+    "\n",
+    "\n",
+    "progress_bar = tqdm.tqdm(total=len(paths), miniters=1)\n",
+    "async with TaskGroup() as task_group:\n",
+    "    for path in paths:\n",
+    "        destination_path = Path(destination / path).with_suffix(\".parquet\")\n",
+    "        if destination_path.exists():\n",
+    "            progress_bar.update()\n",
+    "        else:\n",
+    "            task_group.create_task(\n",
+    "                copy_and_convert(path, source_store, destination_path, progress_bar)\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "287045de",
+   "metadata": {},
+   "source": [
+    "Alright!\n",
+    "Let's see what we got."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "85348f6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 files errored\n",
+      "The 5134 stac-geoparquet files are 3.6 GB\n",
+      "That's 12.53% of the original size\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os.path\n",
+    "\n",
+    "print(f\"{len(missed_paths)} files errored\")\n",
+    "\n",
+    "count = 0\n",
+    "size = 0\n",
+    "for path in destination.glob(\"**/*.parquet\"):\n",
+    "    count += 1\n",
+    "    size += os.path.getsize(path)\n",
+    "\n",
+    "print(f\"The {count} stac-geoparquet files are {humanize.naturalsize(size)}\")\n",
+    "print(f\"That's {100 * size / sum(sizes):.2f}% of the original size\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af91a914",
+   "metadata": {},
+   "source": [
+    "Very cool.\n",
+    "We can use [DuckDB](https://duckdb.org/) to search into the files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6db49d82",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌──────────────┐\n",
+       "│ count_star() │\n",
+       "│    int64     │\n",
+       "├──────────────┤\n",
+       "│      9907260 │\n",
+       "└──────────────┘"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import duckdb\n",
+    "\n",
+    "duckdb.sql(f\"select count(*) from read_parquet('{destination}/**/*.parquet')\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa5e6374",
+   "metadata": {},
+   "source": [
+    "DuckDB recommends that each partition contains [100 MB](https://duckdb.org/docs/stable/data/partitioning/partitioned_writes.html#partitioned-writes) of data, but some of our files are much smaller.\n",
+    "Let's re-partition our data by year to get larger partitions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e555066c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "partitioned_destination = \"../../data/its-live-partitioned\"\n",
+    "# We limit the number of open files to not hose our processor, it defaults to 100\n",
+    "duckdb.sql(\"set partitioned_write_max_open_files = 4;\")\n",
+    "duckdb.sql(\n",
+    "    f\"copy (select *, year(datetime) as year from read_parquet('{destination}/**/*.parquet', union_by_name=true)) to '{partitioned_destination}' (format parquet, partition_by (year), overwrite_or_ignore)\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f40a2807",
+   "metadata": {},
+   "source": [
+    "We can now query by year effectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "80ead1a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌──────────────┐\n",
+       "│ count_star() │\n",
+       "│    int64     │\n",
+       "├──────────────┤\n",
+       "│      1278258 │\n",
+       "└──────────────┘"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duckdb.sql(\n",
+    "    f\"select count(*) from read_parquet('{partitioned_destination}/**/*.parquet') where year = 2024\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0dd70787",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌──────────────────────┐\n",
+       "│     column_name      │\n",
+       "│       varchar        │\n",
+       "├──────────────────────┤\n",
+       "│ type                 │\n",
+       "│ stac_version         │\n",
+       "│ stac_extensions      │\n",
+       "│ id                   │\n",
+       "│ version              │\n",
+       "│ proj:code            │\n",
+       "│ links                │\n",
+       "│ assets               │\n",
+       "│ collection           │\n",
+       "│ datetime             │\n",
+       "│    ·                 │\n",
+       "│    ·                 │\n",
+       "│    ·                 │\n",
+       "│ platform             │\n",
+       "│ scene_1_id           │\n",
+       "│ scene_2_id           │\n",
+       "│ scene_1_path_row     │\n",
+       "│ scene_2_path_row     │\n",
+       "│ sat:orbit_state      │\n",
+       "│ percent_valid_pixels │\n",
+       "│ bbox                 │\n",
+       "│ geometry             │\n",
+       "│ year                 │\n",
+       "├──────────────────────┤\n",
+       "│  27 rows (20 shown)  │\n",
+       "└──────────────────────┘"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duckdb.sql(\n",
+    "    f\"select column_name from (describe select * from read_parquet('{partitioned_destination}/**/*.parquet'))\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c9c8844",
+   "metadata": {},
+   "source": [
+    "Each partition's files is still **stac-geoparquet**, so we can read them back in if we want.\n",
+    "Most of them are pretty big, so we intentionally pick a smaller one for this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cb353cb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23\n"
+     ]
+    }
+   ],
+   "source": [
+    "item_collection = await rustac.read(\n",
+    "    str(Path(partitioned_destination) / \"year=1982\" / \"data_0.parquet\")\n",
+    ")\n",
+    "print(len(item_collection[\"features\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4886e961",
+   "metadata": {},
+   "source": [
+    "## Searching\n",
+    "\n",
+    "One of **rustac**'s features is the ability to use [STAC API search](https://api.stacspec.org/v1.0.0/item-search/) against **stac-geoparquet** files, no server required.\n",
+    "We can use a [cql2](https://github.com/developmentseed/cql2-rs/) filter to query by attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "89b7e0b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "39696\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cql2\n",
+    "\n",
+    "href = str(Path(partitioned_destination) / \"**\" / \"*.parquet\")\n",
+    "cql2_json = cql2.parse_text(\"percent_valid_pixels=100\").to_json()\n",
+    "items = await rustac.search(href, filter=cql2_json)\n",
+    "print(len(items))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "569bc117",
+   "metadata": {},
+   "source": [
+    "If we know we're going to go to a **geopandas** `GeoDataFrame`, we can search directly to an **arrow** table to make things a bit more efficient.\n",
+    "To do so, we'll need to use **rustac**'s `DuckdbClient`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c7e2f43a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from rustac import DuckdbClient\n",
+    "from geopandas import GeoDataFrame\n",
+    "\n",
+    "client = DuckdbClient()\n",
+    "table = client.search_to_arrow(href, filter=cql2_json)\n",
+    "data_frame = GeoDataFrame.from_arrow(table)\n",
+    "data_frame.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mkdocs.yml b/mkdocs.yml
index 007b52f..8de9484 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -23,6 +23,7 @@ nav:
       - notebooks/read.ipynb
       - notebooks/store.ipynb
       - notebooks/stac-geoparquet.ipynb
+      - notebooks/its-live.ipynb
       - notebooks/search.ipynb
   - API:
       - api/index.md
diff --git a/pyproject.toml b/pyproject.toml
index 0847ec4..edd89d9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -70,6 +70,7 @@ dev = [
 docs = [
     "arro3-core>=0.4.5",
     "contextily>=1.6.2",
+    "cql2>=0.3.7",
     "duckdb>=1.3.0",
     "griffe>=1.6.0",
     "humanize>=4.12.1",
diff --git a/uv.lock b/uv.lock
index 9af3deb..2373a59 100644
--- a/uv.lock
+++ b/uv.lock
@@ -408,6 +408,74 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c1/31/1ae946f11dfbd229222e6d6ad8e7bd1891d3d48bde5fbf7a0beb9491f8e3/contourpy-1.3.1-cp313-cp313t-win_amd64.whl", hash = "sha256:287ccc248c9e0d0566934e7d606201abd74761b5703d804ff3df8935f523d546", size = 236668, upload-time = "2024-11-12T10:57:39.061Z" },
 ]
 
+[[package]]
+name = "cql2"
+version = "0.3.7"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1d/0b/0011b4e45c888e1b8b9eaaec05716bb0b6f28c4110923b4654a7b8957ed3/cql2-0.3.7.tar.gz", hash = "sha256:8d70cc6005c379f22247e7f6d4afeb1455cd3d65f88c83326ac10e5aa0453a42", size = 157195, upload-time = "2025-05-19T17:37:21.076Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c1/72/8a90f9332eecc13aabfaf065d774049699ba4ed99780d037a29b402790a4/cql2-0.3.7-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:57e31ca5271e80a541e3fb7db4b02cce3aa390adbc2e215cff6ad2935f80ff70", size = 2564289, upload-time = "2025-05-19T17:36:20.065Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/26/2df495004633ac655bef6960d2fb20c5250bdbe3e37f45af5344d1322e5c/cql2-0.3.7-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0e5800ea76cb239e97a5127246345811426d7d36589ec5ec20c522ab3a3edcc0", size = 2446368, upload-time = "2025-05-19T17:36:15.804Z" },
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+
 [[package]]
 name = "cssselect2"
 version = "0.7.0"
@@ -2305,6 +2373,7 @@ dev = [
 docs = [
     { name = "arro3-core" },
     { name = "contextily" },
+    { name = "cql2" },
     { name = "duckdb" },
     { name = "griffe" },
     { name = "humanize" },
@@ -2340,6 +2409,7 @@ dev = [
 docs = [
     { name = "arro3-core", specifier = ">=0.4.5" },
     { name = "contextily", specifier = ">=1.6.2" },
+    { name = "cql2", specifier = ">=0.3.7" },
     { name = "duckdb", specifier = ">=1.3.0" },
     { name = "griffe", specifier = ">=1.6.0" },
     { name = "humanize", specifier = ">=4.12.1" },