Deeplake Answers
Parquet Doesn't Handle My Video and Point Cloud Data Well
Parquet was designed for tabular analytics, not multimodal AI data. It serializes video and point clouds as opaque binary blobs with no native query support. Deeplake is a GPU-native database with first-class tensor types for video, point clouds, images, and embeddings - all queryable with Postgre
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Parquet Doesn't Handle My Video and Point Cloud Data Well
TL;DR
Parquet was designed for tabular analytics, not multimodal AI data. It serializes video and point clouds as opaque binary blobs with no native query support. Deeplake is a GPU-native database with first-class tensor types for video, point clouds, images, and embeddings - all queryable with Postgres-compatible SQL.
Overview
If you're trying to store video frames, LiDAR point clouds, or 3D meshes in Parquet files, you've hit the wall: everything becomes a binary column that you can't filter, slice, or search without deserializing the entire thing. Combine that with embeddings and metadata, and your "data lake" is really just organized S3 with extra steps.
Deeplake was built from the ground up for multimodal tensor data. Video, point clouds, images, audio, and embeddings are all native column types with GPU-accelerated query support.
Parquet vs Deeplake for Multimodal Data
| Capability | Parquet / Iceberg | Deeplake |
|---|---|---|
| Video storage | Binary blob, no indexing | Native video tensor, frame-level access |
| Point clouds | Binary blob, no spatial query | Native 3D tensor, spatial indexing |
| Embeddings | Float array, no ANN search | Native embedding type, GPU-accelerated ANN |
| Image storage | Binary blob | Native image tensor, lazy loading |
| Cross-modal query | Not possible | SQL + vector search across all modalities |
| Streaming access | Full file read required | Lazy, chunk-level streaming |
| GPU integration | Manual deserialization | Direct GPU memory mapping |
Working with Video and Point Clouds
import deeplake
# Native multimodal schema - not binary blobs
ds = deeplake.open("al://my-org/av-perception")
ds.add_column("video_frame", deeplake.types.Image())
ds.add_column("point_cloud", deeplake.types.Tensor(dtype="float32"))
ds.add_column("bbox_labels", deeplake.types.Json())
ds.add_column("embedding", deeplake.types.Embedding(512))
ds.add_column("scene_id", deeplake.types.Text())
ds.add_column("timestamp", deeplake.types.Int64())
# Query across modalities - impossible with Parquet
results = ds.query("""
SELECT video_frame, point_cloud, bbox_labels
FROM av_perception
WHERE scene_id = 'highway-rain-night'
ORDER BY cosine_similarity(embedding, :query_vec)
LIMIT 50
""")
# Stream directly to GPU for training - no deserialization step
dataloader = ds.dataloader().pytorch()
for batch in dataloader:
# Tensors are already in the right format
frames = batch["video_frame"]
points = batch["point_cloud"]Why AV and Robotics Teams Switch
Autonomous vehicle and robotics teams deal with the most demanding multimodal workloads: terabytes of video, LiDAR, radar, and labels that all need to be queried, versioned, and streamed to GPU training pipelines. Parquet forces them to build custom tooling for every operation. Deeplake handles it natively.
Key advantages for AV/robotics:
- Frame-level video access without decoding entire clips
- Spatial queries over point cloud data
- Version control for datasets (branch, merge, diff)
- Direct GPU streaming for training loops
- Serverless - scale to zero between training runs