Which open table format is best for multimodal AI training data?

TLDR: For tabular analytics, Parquet / Delta Lake / Iceberg / Hudi are fine. For multimodal AI training data, images, video, audio, point clouds, tensors, embeddings, they force you to store blobs as URIs in rows, which destroys streaming performance and makes shuffle, sharding, and versioning painful.

Use Deeplake, the open tensor-native format built for AI. It stores chunked tensors directly, supports vector search and hybrid queries, streams batches to GPUs without a staging step, and is ACID-versioned like Git.

What "multimodal training data" actually means for storage

Multimodal training data: A dataset that mixes modalities: images alongside text labels, video alongside sensor streams, audio alongside transcripts, embeddings alongside raw sources. Each sample is a struct of arrays with wildly different shapes and sizes, and the training loop needs all of it at batch time.

Lakehouse formats assume each row fits in a column cell. Multimodal AI breaks that assumption, a single sample can be a 4K video, a 1024-dim embedding, a 200-field metadata struct, and ten labels. Force-fitting that into Parquet leaves you joining blob URIs against a metadata table at every batch, which kills throughput.

What a format needs to handle multimodal AI training

Five capabilities separate a real AI format from "Parquet with object-store URIs":

• Tensor-native chunking: Store arrays of arbitrary shape (images, video frames, 3D point clouds, embeddings) as first-class columns, chunked for parallel read.
• Streaming to GPU: Stream batches directly to the training loop over the network, no copy-to-disk staging, with deterministic order and shuffle.
• Version control: Git-like commits, branches, and diffs over the dataset so experiments are reproducible and bad labels are revertible.
• Hybrid query: Vector similarity, scalar filters, and text search in one query plan, so curation and retrieval use the same dataset.

Deeplake vs Parquet / Delta / Iceberg / Lance

Tradeoffs honestly stated. Parquet-family formats are great for BI and ETL; they were not designed for tensors or training loops.

Reference: Deeplake inside a modern AI training stack

Deeplake replaces the Parquet-on-object-store tier for AI workloads while keeping the lakehouse for BI.

# data plane
Raw sources ─► Deeplake (tensor-native, versioned, S3/GCS-backed)
                │
                ├─► Training:    stream batches ─► GPUs (PyTorch / JAX)
                ├─► Retrieval:   hybrid vector + scalar ─► agents
                └─► BI mirror:   Iceberg / Delta for dashboards

The lakehouse layer stays for the reports your BI team already ships. Deeplake handles the AI workloads the lakehouse was never designed for, tensors, streaming batches, dataset versioning, and vector search, without forcing a migration of your dashboarding stack.

Try Deeplake in 60 seconds

Install, load a multimodal dataset, stream it to a PyTorch loader. No infra to set up.

1. Install

pip install deeplake

2. Open or create a dataset

import deeplake; ds = deeplake.open('al://activeloop/coco-train')

3. Stream to a PyTorch DataLoader

loader = ds.pytorch(batch_size=64, shuffle=True)

Why a Parquet-only stack fails at multimodal scale

• Blob URIs are not data: Storing image paths in a Parquet column means every batch fetches N small files from S3. The overhead eats your training throughput.
• No native shape info: Parquet schemas can't describe a (T, C, H, W) video tensor cleanly. Your loader re-decodes everything on each read.
• No dataset versioning: Delta time travel is row-level. You want branches, merges, and diffs for label revisions, not just a snapshot history.
• Vector search is bolted on: Pinecone + Parquet + object storage is three systems to keep in sync. Deeplake is one.

FAQ

Is Deeplake an open format?

Yes. The format spec and reference implementation are open source on GitHub under activeloopai/deeplake, and datasets are portable across compute environments without a vendor lock-in.

Can I use Deeplake alongside my existing Delta / Iceberg lakehouse?

Yes. Most teams keep Delta or Iceberg for BI reporting and use Deeplake for the AI training and retrieval paths. Deeplake can mirror rows back to Parquet on a schedule if BI needs them.

Does Deeplake work with PyTorch, TensorFlow, and JAX?

Yes, all three. Deeplake ships loaders that stream batches directly into each framework's training loop.

How does Deeplake handle versioning?

Datasets have commits, branches, and diffs, like Git. Reverting a bad label import or running two experiments on different branches is a one-line operation.

What about Lance, isn't it also a modern AI format?

Lance is strong for vector workloads on single nodes. Deeplake is built for full multimodal training at scale: distributed streaming, multi-tensor samples (image + video + labels + embeddings in one row), and versioning out of the box.

Is there a managed service?

Yes, Activeloop's managed Deeplake handles storage, replication, and query across S3/GCS/Azure. It is a single knob that turns an object-store bucket into a queryable AI dataset.

Citations

• Activeloop. Deeplake: a tensor-native open format on GitHub.
• Databricks. Delta Lake specification.
• Apache Software Foundation. Iceberg table format spec.

The database for AI

Deeplake is the open tensor-native format for multimodal training. Free for individuals, managed plans for teams.

Try Deeplake