docs: align odc-stac notebook intro and ending with batch voice

Add a Key benefits list and a We'll setup line to the intro, and close
with a You're done recap, matching the lonboard quickstart's structure.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Author: aboydnw

quickstarts/odc-stac.ipynb

@@ -6,9 +6,17 @@
    "source": [
     "# Building an analysis-ready data cube from Planetary Computer STAC",
     "",
-    "A STAC search returns item metadata: hrefs, dates, cloud cover. Analysis usually wants something else, a single aligned array indexed by time, band, and space, that you can run math across. Two libraries build that cube from STAC items: [odc-stac](https://odc-stac.readthedocs.io/) and [stackstac](https://stackstac.readthedocs.io/). They take the same inputs and produce lazy, Dask-backed xarray objects, but they differ in ways that matter on Planetary Computer data.",
+    "A STAC search returns item metadata: hrefs, dates, cloud cover. Analysis usually wants something else: a single aligned array indexed by time, band, and space that you can run math across. This notebook builds that cube two ways, with [odc-stac](https://odc-stac.readthedocs.io/) and [stackstac](https://stackstac.readthedocs.io/). Key benefits:",
     "",
-    "This notebook loads a few low-cloud [Sentinel-2 L2A](https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a) scenes over Portland with both libraries and compares them. The companion [data cube tutorial](../overview/odc-stac.md) has the full narrative."
+    "1. **Aligned cube**: turn a pile of STAC items into one array indexed by time, band, and space.",
+    "2. **Lazy and Dask-backed**: the cube builds without reading pixels until you compute.",
+    "3. **CRS handling**: odc-stac infers the projection from STAC metadata; stackstac takes an explicit `epsg=`.",
+    "4. **Two shapes**: odc-stac returns a `Dataset` of named `float32` bands; stackstac returns one `float64` `DataArray` with a `band` dimension and every item property as a coordinate.",
+    "5. **Same inputs**: both take the same signed STAC items, so you can pick per pipeline.",
+    "",
+    "We'll load a few low-cloud [Sentinel-2 L2A](https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a) scenes over Portland, Oregon with both libraries and compare CRS handling, shape, dtype, and metadata.",
+    "",
+    "The companion [data cube tutorial](../overview/odc-stac.md) has the full narrative."
    ]
   },
   {
@@ -149,9 +157,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## When to use something else",
+    "## You're done",
+    "",
+    "If both load cells built a cube, you turned four Sentinel-2 scenes into an analysis-ready array two ways: odc-stac's named `float32` `Dataset` and stackstac's `float64` `DataArray` with metadata as coordinates.",
     "",
-    "A cube is the right shape for time-series and multi-band analysis across an area. When you only need pixels from a single scene, the cube machinery is overhead; read the window directly with [async-geotiff](../overview/async-geotiff.md) instead."
+    "Swap in your own bbox, collection, or band list and the same load applies. When you only need pixels from a single scene rather than a cube, the cube machinery is overhead; read the window directly with the [async-geotiff tutorial](../overview/async-geotiff.md) instead."
    ]
   }
  ],