diff --git a/.typos.toml b/.typos.toml
index 72249872a9f7..0a54337dbaf2 100644
--- a/.typos.toml
+++ b/.typos.toml
@@ -156,4 +156,6 @@ extend-ignore-re = [
   "np.arange",        # numpy spells "arrange" wrong
   "phc_[a-zA-Z0-9]*", # Posthog public key
   "PNG.?",            # Workaround for https://github.com/crate-ci/typos/issues/967
+
+  "_LEVL_", # USed in air_traffic_data.py
 ]
diff --git a/examples/manifest.toml b/examples/manifest.toml
index ce8d9b4d3376..d3ae1b172e64 100644
--- a/examples/manifest.toml
+++ b/examples/manifest.toml
@@ -127,6 +127,7 @@ examples = [
   "dicom_mri",
   "blueprint_stocks",
   "arflow",
+  "air_traffic_data",
 ]
 
 [categories.feature-showcase]
@@ -150,6 +151,7 @@ examples = [
   "raw_mesh",
   "graph_lattice",
   "graph_binary_tree",
+  "air_traffic_data",
 ]
 
 # These are examples that we explicitly exclude from our website. You can check that all examples are either included
diff --git a/examples/python/air_traffic_data/.gitignore b/examples/python/air_traffic_data/.gitignore
new file mode 100644
index 000000000000..3af0ccb687b4
--- /dev/null
+++ b/examples/python/air_traffic_data/.gitignore
@@ -0,0 +1 @@
+/data
diff --git a/examples/python/air_traffic_data/README.md b/examples/python/air_traffic_data/README.md
new file mode 100644
index 000000000000..0d774deedd3f
--- /dev/null
+++ b/examples/python/air_traffic_data/README.md
@@ -0,0 +1,49 @@
+<!--[metadata]
+title = "Air traffic data"
+tags = ["2D", "3D", "map", "crs"]
+description = "Display aircraft traffic data"
+thumbnail = "https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/480w.png"
+thumbnail_dimensions = [480, 294]
+channel = "main"
+-->
+
+
+Display air traffic data kindly provided by [INVOLI](https://www.involi.com).
+
+<picture>
+  <img src="https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/full.png" alt="">
+  <source media="(max-width: 480px)" srcset="https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/480w.png">
+  <source media="(max-width: 768px)" srcset="https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/768w.png">
+  <source media="(max-width: 1024px)" srcset="https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/1024w.png">
+  <source media="(max-width: 1200px)" srcset="https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/1200w.png">
+</picture>
+
+This example demonstrates multiple aspects of the Rerun viewer:
+
+- Use of the [map view](https://rerun.io/docs/reference/types/views/map_view).
+- Use of [pyproj](https://pyproj4.github.io/pyproj/stable/) to transform geospatial data from one coordinate system to another.
+- Use [GeoPandas](https://geopandas.org/en/stable/) to load geospatial data into a 3D view.
+- Use [Polars]https://pola.rs) to batch data to be sent via [`rr.send_columns()`](https://rerun.io/docs/howto/logging/send-columns) (use `--batch`).
+
+
+## Run the code
+
+To run this example, make sure you have Python version at least 3.9, the Rerun repository checked out and the latest SDK installed:
+```bash
+pip install --upgrade rerun-sdk  # install the latest Rerun SDK
+git clone git@github.com:rerun-io/rerun.git  # Clone the repository
+cd rerun
+git checkout latest  # Check out the commit matching the latest SDK release
+```
+Install the necessary libraries specified in the requirements file:
+```bash
+pip install -e examples/python/air_traffic_data
+```
+To experiment with the provided example, simply execute the main Python script:
+```bash
+python -m air_traffic_data
+```
+If you wish to customize it, explore additional features, or save it use the CLI with the `--help` option for guidance:
+```bash
+python -m air_traffic_data --help
+```
diff --git a/examples/python/air_traffic_data/air_traffic_data.py b/examples/python/air_traffic_data/air_traffic_data.py
new file mode 100644
index 000000000000..911c4423e69d
--- /dev/null
+++ b/examples/python/air_traffic_data/air_traffic_data.py
@@ -0,0 +1,457 @@
+from __future__ import annotations
+
+import dataclasses
+import io
+import itertools
+import json
+import re
+import typing
+import zipfile
+from argparse import ArgumentParser
+from pathlib import Path
+from typing import Any
+
+import geopandas as gpd
+import numpy as np
+import numpy.typing as npt
+import polars
+import pyproj
+import requests
+import rerun as rr
+import rerun.blueprint as rrb
+import shapely
+from pyproj import CRS, Transformer
+from pyproj.aoi import AreaOfInterest
+from pyproj.database import query_utm_crs_info
+from tqdm import tqdm
+
+DATA_DIR = Path(__file__).parent / "dataset"
+MAP_DATA_DIR = DATA_DIR / "map_data"
+if not DATA_DIR.exists():
+    DATA_DIR.mkdir()
+
+INVOLI_DATASETS = {
+    "10min": "https://storage.googleapis.com/rerun-example-datasets/involi/involi_demo_set_1_10min.zip",
+    "2h": "https://storage.googleapis.com/rerun-example-datasets/involi/involi_demo_set_2_2h.zip",
+}
+
+
+def download_with_progress(url: str, what: str) -> io.BytesIO:
+    """Download file with tqdm progress bar."""
+    chunk_size = 1024 * 1024
+    resp = requests.get(url, stream=True)
+    total_size = int(resp.headers.get("content-length", 0))
+    with tqdm(
+        desc=f"Downloading {what}…",
+        total=total_size,
+        unit="iB",
+        unit_scale=True,
+        unit_divisor=1024,
+    ) as progress:
+        download_file = io.BytesIO()
+        for data in resp.iter_content(chunk_size):
+            download_file.write(data)
+            progress.update(len(data))
+
+    download_file.seek(0)
+    return download_file
+
+
+def shapely_geom_to_numpy(geom: shapely.Geometry) -> list[npt.NDArray[np.float64]]:
+    """Convert shapely objects to numpy array suitable for logging as line batches."""
+
+    if geom.geom_type == "Polygon":
+        return [np.array(geom.exterior.coords)] + [np.array(interior.coords) for interior in geom.interiors]
+    elif geom.geom_type == "MultiPolygon":
+        res = []
+        for poly in geom.geoms:
+            res.extend(shapely_geom_to_numpy(poly))
+        return res
+    else:
+        print(f"Warning: unknown Shapely object {geom}")
+        return []
+
+
+def log_region_boundaries_for_country(
+    country_code: str, level: int, color: tuple[float, float, float], crs: CRS
+) -> None:
+    """Log some boundaries for the given country and level."""
+
+    def download_eu_map_data() -> None:
+        """Download some basic EU map data."""
+
+        if MAP_DATA_DIR.exists():
+            return
+
+        EU_MAP_DATA_URL = "https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/ref-nuts-2021-01m.json.zip"
+        zip_data = download_with_progress(EU_MAP_DATA_URL, "map data")
+        with zipfile.ZipFile(zip_data) as zip_ref:
+            zip_ref.extractall(MAP_DATA_DIR)
+
+    download_eu_map_data()
+
+    # cspell:disable-next-line
+    map_data = gpd.read_file(MAP_DATA_DIR / f"NUTS_RG_01M_2021_4326_LEVL_{level}.json").set_crs("epsg:4326").to_crs(crs)
+
+    for i, row in map_data[map_data.CNTR_CODE == country_code].iterrows():
+        entity_path = f"region_boundaries/{country_code}/{level}/{row.NUTS_ID}"
+        lines = shapely_geom_to_numpy(row.geometry)
+        rr.log(entity_path + "/2D", rr.LineStrips2D(lines, colors=color), static=True)
+        rr.log(
+            entity_path + "/3D",
+            rr.LineStrips3D(
+                [np.hstack([line, np.zeros((len(line), 1))]) for line in lines],
+                colors=color,
+            ),
+            static=True,
+        )
+        metadata = row.to_dict()
+        metadata.pop("geometry")
+        rr.log(entity_path, rr.AnyValues(**metadata), static=True)
+
+
+@dataclasses.dataclass
+class Measurement:
+    """One measurement loaded from INVOLI data. Corresponds to an "aircraft" record."""
+
+    icao_id: str
+    latitude: float | None
+    longitude: float | None
+    barometric_altitude: float | None
+    wg84_altitude: float | None
+    course: float | None
+    ground_speed: float | None
+    vertical_speed: float | None
+    ground_status: str | None
+    timestamp: float
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> Measurement:
+        return cls(
+            icao_id=data["ids"]["icao"],
+            latitude=data.get("latitude"),
+            longitude=data.get("longitude"),
+            barometric_altitude=data.get("barometric_altitude"),
+            wg84_altitude=data.get("wg84_altitude"),
+            course=data.get("course"),
+            ground_speed=data.get("ground_speed"),
+            vertical_speed=data.get("vertical_speed"),
+            ground_status=data.get("ground_status"),
+            timestamp=data["timestamp"][0] + data["timestamp"][1] / 1e9,
+        )
+
+
+def find_best_utm_crs(measurements: list[Measurement]) -> CRS:
+    """Returns the best UTM coordinates reference system given a list of measurements."""
+
+    def get_area_of_interest(measurements: list[Measurement]) -> AreaOfInterest:
+        """Compute the span of coordinates for all provided measurements."""
+
+        print("Computing area of interest...")
+        all_long_lat = [
+            (a.longitude, a.latitude) for a in measurements if a.latitude is not None and a.longitude is not None
+        ]
+        return AreaOfInterest(
+            west_lon_degree=min(x[0] for x in all_long_lat),
+            south_lat_degree=min(x[1] for x in all_long_lat),
+            east_lon_degree=max(x[0] for x in all_long_lat),
+            north_lat_degree=max(x[1] for x in all_long_lat),
+        )
+
+    area_of_interest = get_area_of_interest(measurements)
+    utm_crs_list = query_utm_crs_info(
+        datum_name="WGS 84",
+        area_of_interest=area_of_interest,
+    )
+
+    return CRS.from_epsg(utm_crs_list[0].code)
+
+
+def load_measurements(paths: list[Path]) -> list[Measurement]:
+    """Load measurements from a bunch of json files."""
+    all_measurements = []
+    for path in tqdm(paths, "Loading measurements"):
+        data = json.loads(path.read_text())
+        for data_rec in data:
+            for aircraft in data_rec["records"]:
+                all_measurements.append(Measurement.from_dict(aircraft["aircraft"]))
+
+    return all_measurements
+
+
+def get_paths_for_directory(directory: Path) -> list[Path]:
+    """
+    Get a sorted list of JSON file by recursively walking the provided directory.
+
+    Note: technically, we don't need the list to be sorted as Rerun accepts out of order data. However, it comes at a
+    (small) performance cost and any (cheap) sorting on the logging end is always better.
+    """
+
+    def atoi(text: str) -> int | str:
+        return int(text) if text.isdigit() else text
+
+    def natural_keys(path: Path) -> list[int | str]:
+        """
+        Human sort.
+
+        alist.sort(key=natural_keys) sorts in human order
+        https://nedbatchelder.com/blog/200712/human_sorting.html
+        (See Toothy's implementation in the comments)
+        """
+        return [atoi(c) for c in re.split(r"(\d+)", str(path))]
+
+    return sorted(directory.rglob("*.json"), key=natural_keys)
+
+
+class Logger(typing.Protocol):
+    def process_measurement(self, measurement: Measurement) -> None:
+        pass
+
+    def flush(self) -> None:
+        pass
+
+
+# ================================================================================================
+# Simple logger
+
+
+class MeasurementLogger:
+    """Logger class that uses regular `rr.log` calls."""
+
+    def __init__(self, proj: pyproj.Transformer, raw: bool):
+        self._proj = proj
+        self._raw = raw
+
+        self._ignored_fields = [
+            "icao_id",  # already the entity's path
+            "timestamp",  # already the clock's value
+        ]
+
+    def process_measurement(self, measurement: Measurement) -> None:
+        rr.set_time_seconds("unix_time", measurement.timestamp)
+
+        if self._raw:
+            metadata = dataclasses.asdict(measurement)
+        else:
+            metadata = dataclasses.asdict(
+                measurement,
+                dict_factory=lambda x: {k: v for (k, v) in x if k not in self._ignored_fields and v is not None},
+            )
+
+        entity_path = f"aircraft/{measurement.icao_id}"
+
+        if (
+            measurement.latitude is not None
+            and measurement.longitude is not None
+            and measurement.barometric_altitude is not None
+        ):
+            rr.log(
+                entity_path,
+                rr.Points3D([
+                    self._proj.transform(
+                        measurement.longitude,
+                        measurement.latitude,
+                        measurement.barometric_altitude,
+                    )
+                ]),
+                rr.GeoPoints(lat_lon=[measurement.latitude, measurement.longitude]),
+            )
+
+        if len(metadata) > 0:
+            rr.log(entity_path, rr.AnyValues(**metadata))
+
+        if measurement.barometric_altitude is not None:
+            rr.log(
+                entity_path + "/barometric_altitude",
+                rr.Scalar(measurement.barometric_altitude),
+            )
+
+    def flush(self) -> None:
+        pass
+
+
+# ================================================================================================
+# Batch logger
+
+
+class MeasurementBatchLogger:
+    """Logger class that batches measurements and uses `rr.send_columns` calls."""
+
+    def __init__(self, proj: pyproj.Transformer, batch_size: int = 8192):
+        self._proj = proj
+        self._batch_size = batch_size
+        self._measurements: list[Measurement] = []
+        self._position_indicators: set[str] = set()
+
+    def process_measurement(self, measurement: Measurement) -> None:
+        self._measurements.append(measurement)
+
+        if len(self._measurements) >= 8192:
+            self.flush()
+
+    def flush(self) -> None:
+        # !!! the raw data is not sorted by timestamp, so we sort it here
+        df = polars.DataFrame(self._measurements).sort("timestamp")
+        self._measurements = []
+
+        for (icao_id,), group in df.group_by("icao_id"):
+            icao_id = str(icao_id)
+
+            # Note: this splitting in 3 different functions is due to the pattern of nulls in the raw data.
+            self.log_position_and_altitude(group, icao_id)
+            self.log_ground_status(group, icao_id)
+            self.log_metadata(group, icao_id)
+
+    def log_position_and_altitude(self, df: polars.DataFrame, icao_id: str) -> None:
+        entity_path = f"aircraft/{icao_id}"
+        df = df["timestamp", "latitude", "longitude", "barometric_altitude"].drop_nulls()
+
+        if df.height == 0:
+            return
+
+        if icao_id not in self._position_indicators:
+            rr.log(entity_path, [rr.archetypes.Points3D.indicator(), rr.archetypes.GeoPoints.indicator()], static=True)
+            self._position_indicators.add(icao_id)
+
+        timestamps = rr.TimeSecondsColumn("unix_time", df["timestamp"].to_numpy())
+        pos = self._proj.transform(df["longitude"], df["latitude"], df["barometric_altitude"])
+        positions = rr.components.Position3DBatch(np.vstack(pos).T)
+
+        lat_lon = rr.components.LatLonBatch(np.vstack((df["latitude"], df["longitude"])).T)
+
+        raw_coordinates = rr.AnyValues(
+            latitude=df["latitude"].to_numpy(),
+            longitude=df["longitude"].to_numpy(),
+            barometric_altitude=df["barometric_altitude"].to_numpy(),
+        )
+
+        rr.send_columns(
+            entity_path,
+            [timestamps],
+            [
+                positions,
+                lat_lon,
+                *raw_coordinates.as_component_batches(),
+            ],
+        )
+
+        rr.send_columns(
+            entity_path + "/barometric_altitude",
+            [timestamps],
+            [rr.components.ScalarBatch(df["barometric_altitude"].to_numpy())],
+        )
+
+    def log_ground_status(self, df: polars.DataFrame, icao_id: str) -> None:
+        entity_path = f"aircraft/{icao_id}"
+        df = df["timestamp", "ground_status"].drop_nulls()
+
+        timestamps = rr.TimeSecondsColumn("unix_time", df["timestamp"].to_numpy())
+        batches = rr.AnyValues(ground_status=df["ground_status"].to_numpy())
+
+        rr.send_columns(entity_path, [timestamps], batches.as_component_batches())
+
+    def log_metadata(self, df: polars.DataFrame, icao_id: str) -> None:
+        entity_path = f"aircraft/{icao_id}"
+        df = df["timestamp", "course", "ground_speed", "vertical_speed"].drop_nulls()
+
+        metadata = rr.AnyValues(
+            course=df["course"].to_numpy(),
+            ground_speed=df["ground_speed"].to_numpy(),
+            vertical_speed=df["vertical_speed"].to_numpy(),
+        )
+
+        rr.send_columns(
+            entity_path,
+            [rr.TimeSecondsColumn("unix_time", df["timestamp"].to_numpy())],
+            metadata.component_batches,
+        )
+
+
+# ================================================================================================
+
+
+def log_everything(paths: list[Path], raw: bool, batch: bool, batch_size: int) -> None:
+    measurements = load_measurements(paths)
+    utm_crs = find_best_utm_crs(measurements)
+
+    proj = Transformer.from_crs("EPSG:4326", utm_crs, always_xy=True)
+
+    rr.set_time_seconds("unix_time", 0)
+    for country_code, (level, color) in itertools.product(["DE", "CH"], [(0, (1, 0.5, 0.5))]):
+        log_region_boundaries_for_country(country_code, level, color, utm_crs)
+
+    # Exaggerate altitudes
+    rr.log("aircraft", rr.Transform3D(scale=[1, 1, 10]), static=True)
+
+    if batch:
+        logger: Logger = MeasurementBatchLogger(proj, batch_size)
+    else:
+        logger = MeasurementLogger(proj, raw)
+
+    for measurement in tqdm(measurements, "Logging measurements"):
+        if measurement.icao_id is None:
+            continue
+
+        logger.process_measurement(measurement)
+    logger.flush()
+
+
+def main() -> None:
+    parser = ArgumentParser(description="Visualize INVOLI data")
+    parser.add_argument(
+        "--dataset",
+        choices=INVOLI_DATASETS.keys(),
+        default="2h",
+        help="Which dataset to automatically download and visualize",
+    )
+    parser.add_argument(
+        "--raw",
+        action="store_true",
+        help="If true, logs the raw data with all its issues (useful to stress edge cases in the viewer)",
+    )
+    parser.add_argument(
+        "--batch",
+        action="store_true",
+        default=True,
+        help="If true, use the batch logger function (rerun 0.18 required)",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=8192,
+        help="Batch size for the batch logger",
+    )
+    parser.add_argument(
+        "--dir",
+        type=Path,
+        help="Use this directory of data instead of downloading a dataset",
+    )
+    rr.script_add_args(parser)
+    args = parser.parse_args()
+
+    if args.dir:
+        dataset_directory = args.dir
+    else:
+        dataset = args.dataset
+        dataset_ulr = INVOLI_DATASETS[dataset]
+        dataset_directory = DATA_DIR / dataset
+        if not dataset_directory.exists():
+            dataset_directory.mkdir()
+            zip_data = download_with_progress(dataset_ulr, f"dataset {dataset}")
+            with zipfile.ZipFile(zip_data) as zip_ref:
+                zip_ref.extractall(dataset_directory)
+
+    # TODO(ab): this blueprint would be massively improved by setting the 3D view's orbit point to FRA's coordinates.
+    blueprint = rrb.Vertical(
+        rrb.Horizontal(rrb.Spatial3DView(origin="/"), rrb.MapView(origin="/")),
+        rrb.TimeSeriesView(origin="/aircraft"),
+        row_shares=[3, 1],
+    )
+    rr.script_setup(args, "rerun_example_air_traffic_data", default_blueprint=blueprint)
+
+    paths = get_paths_for_directory(dataset_directory)
+    log_everything(paths, args.raw, args.batch, args.batch_size)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/python/air_traffic_data/pyproject.toml b/examples/python/air_traffic_data/pyproject.toml
new file mode 100644
index 000000000000..bc34559a1386
--- /dev/null
+++ b/examples/python/air_traffic_data/pyproject.toml
@@ -0,0 +1,22 @@
+[project]
+name = "air_traffic_data"
+version = "0.1.0"
+readme = "README.md"
+dependencies = [
+  "geopandas",
+  "numpy",
+  "polars",
+  "pyproj",
+  "requests",
+  "rerun-sdk",
+  "shapely",
+  "tqdm",
+]
+
+[project.scripts]
+air_traffic_data = "air_traffic_data:main"
+
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
diff --git a/pixi.lock b/pixi.lock
index c249dc8f45e1..7ea327375897 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -2136,6 +2136,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.2-heb4867d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hebfffa5_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.27-h54b06d7_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2
@@ -2275,11 +2276,13 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-hba22ea6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pip-24.2-pyh8b19718_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda
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+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/pyproj-3.7.0-py311hb4b81e0_0.conda
+  sha256: c2c06cca4ce9fd72f1bf8d69703a8c5c2252efd497619d09f47d95d18d3cccf9
+  md5: e3c2e5ded5b6e4b3c2600d033929645c
+  depends:
+  - __osx >=11.0
+  - certifi
+  - proj >=9.5.0,<9.6.0a0
+  - python >=3.11,<3.12.0a0
+  - python >=3.11,<3.12.0a0 *_cpython
+  - python_abi 3.11.* *_cp311
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pyproj?source=hash-mapping
+  size: 501267
+  timestamp: 1727795522741
+- kind: conda
+  name: pyproj
+  version: 3.7.0
+  build: py311hf06f8e6_0
+  subdir: linux-aarch64
+  url: https://conda.anaconda.org/conda-forge/linux-aarch64/pyproj-3.7.0-py311hf06f8e6_0.conda
+  sha256: 1138944dff2e206cf18ba172e3f2723ce7bafa48f1aee78e01ec9d78e7626e80
+  md5: cce0e19078131aec5b6f7ece87df8120
+  depends:
+  - certifi
+  - libgcc >=13
+  - proj >=9.5.0,<9.6.0a0
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pyproj?source=hash-mapping
+  size: 552175
+  timestamp: 1727797057849
 - kind: pypi
   name: pyquaternion
   version: 0.9.9
@@ -37079,6 +37519,96 @@ packages:
   - certifi
   - jsonschema>=3.0
   requires_python: '>=3.6'
+- kind: conda
+  name: sqlite
+  version: 3.46.1
+  build: h2466b09_0
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/sqlite-3.46.1-h2466b09_0.conda
+  sha256: fdee2e0c16ece695fde231d80242121b5ff610a4f66164f931e2a7622815c3ae
+  md5: 19c50225f5fbbb15d80063a68e52c8bb
+  depends:
+  - libsqlite 3.46.1 h2466b09_0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: Unlicense
+  purls: []
+  size: 886067
+  timestamp: 1725354209514
+- kind: conda
+  name: sqlite
+  version: 3.46.1
+  build: h3b4c4e4_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/sqlite-3.46.1-h3b4c4e4_0.conda
+  sha256: 91c8becaddc4593adba50eb27c4e47deafd879cfc3a569cc6db767b5ee6d8146
+  md5: 78996531776f6a277cac5a14cf590b6a
+  depends:
+  - __osx >=11.0
+  - libsqlite 3.46.1 hc14010f_0
+  - libzlib >=1.3.1,<2.0a0
+  - ncurses >=6.5,<7.0a0
+  - readline >=8.2,<9.0a0
+  license: Unlicense
+  purls: []
+  size: 822440
+  timestamp: 1725353761204
+- kind: conda
+  name: sqlite
+  version: 3.46.1
+  build: h578a6b9_0
+  subdir: linux-aarch64
+  url: https://conda.anaconda.org/conda-forge/linux-aarch64/sqlite-3.46.1-h578a6b9_0.conda
+  sha256: ae45a7f2aefff9570f9330c0c167085228dadc7754c634a949358b7ef6f4a723
+  md5: 6eed5c8ca8a58cd77a6f515997a4173a
+  depends:
+  - libgcc >=13
+  - libsqlite 3.46.1 hc4a20ef_0
+  - libzlib >=1.3.1,<2.0a0
+  - ncurses >=6.5,<7.0a0
+  - readline >=8.2,<9.0a0
+  license: Unlicense
+  purls: []
+  size: 1068759
+  timestamp: 1725354119401
+- kind: conda
+  name: sqlite
+  version: 3.46.1
+  build: h9eae976_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.46.1-h9eae976_0.conda
+  sha256: 8c6245f988a2e1f4eef8456726b9cc46f2462448e61daa4bad2f9e4ca601598a
+  md5: b2b3e737da0ae347e16ef1970a5d3f14
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libsqlite 3.46.1 hadc24fc_0
+  - libzlib >=1.3.1,<2.0a0
+  - ncurses >=6.5,<7.0a0
+  - readline >=8.2,<9.0a0
+  license: Unlicense
+  purls: []
+  size: 859188
+  timestamp: 1725353670478
+- kind: conda
+  name: sqlite
+  version: 3.46.1
+  build: he26b093_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/sqlite-3.46.1-he26b093_0.conda
+  sha256: 668dcc8c38aabf42de440f682de4afe11f390b1dc5b49e09b34501bbf19571c8
+  md5: 56a8cc349cf8e2310ee0e52f90247dab
+  depends:
+  - __osx >=10.13
+  - libsqlite 3.46.1 h4b8f8c9_0
+  - libzlib >=1.3.1,<2.0a0
+  - ncurses >=6.5,<7.0a0
+  - readline >=8.2,<9.0a0
+  license: Unlicense
+  purls: []
+  size: 912164
+  timestamp: 1725353686354
 - kind: pypi
   name: stack-data
   version: 0.6.3
diff --git a/pixi.toml b/pixi.toml
index 510a482e9c10..8dd5906e6dc4 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -602,6 +602,9 @@ macos = "12.0" # needed for scipy
 [feature.examples-common.dependencies]
 python = "=3.11"
 
+# TODO(ab): otherwise pixi will try to build from source and fail, not sure why (used by air_traffic_data)
+pyproj = ">=3.7.0"
+
 [feature.examples-common.pypi-dependencies]
 # External deps
 jupyter = ">=1.0"
@@ -612,6 +615,7 @@ mesh-to-sdf = { git = "https://github.com/marian42/mesh_to_sdf.git" }
 
 # Rerun libs
 
+air_traffic_data = { path = "examples/python/air_traffic_data", editable = true }
 arkit_scenes = { path = "examples/python/arkit_scenes", editable = true }
 blueprint = { path = "examples/python/blueprint", editable = true }
 blueprint_stocks = { path = "examples/python/blueprint_stocks", editable = true }
diff --git a/rerun_py/rerun_sdk/rerun/_send_columns.py b/rerun_py/rerun_sdk/rerun/_send_columns.py
index cd4e4e2df01a..f180b498d7c1 100644
--- a/rerun_py/rerun_sdk/rerun/_send_columns.py
+++ b/rerun_py/rerun_sdk/rerun/_send_columns.py
@@ -5,7 +5,7 @@
 import pyarrow as pa
 import rerun_bindings as bindings
 
-from ._baseclasses import Archetype, ComponentBatchMixin, ComponentColumn
+from ._baseclasses import Archetype, ComponentBatchLike, ComponentBatchMixin, ComponentColumn
 from ._log import IndicatorComponentBatch
 from .any_value import AnyBatchValue
 from .error_utils import catch_and_log_exceptions
@@ -121,7 +121,7 @@ def as_arrow_array(self) -> pa.Array:
 def send_columns(
     entity_path: str,
     times: Iterable[TimeColumnLike],
-    components: Iterable[Union[ComponentBatchMixin, ComponentColumn, AnyBatchValue]],
+    components: Iterable[Union[ComponentBatchLike]],
     recording: RecordingStream | None = None,
     strict: bool | None = None,
 ) -> None: