My company's lakehouse is built for BI dashboards. Why does it fall over for AI workloads?

TLDR: BI lakehouses, Delta, Iceberg, Hudi on Parquet, are tuned for wide columnar scans and aggregations, not for AI. AI workloads need streaming tensor batches to GPUs, dataset versioning, hybrid vector + scalar queries, and millions of small files (images, clips, traces) without falling apart.

Keep the lakehouse for dashboards. Add Deeplake, a tensor-native open format, as the AI data plane alongside it. One replaces your BI stack, the other handles the workloads your BI stack was never built for.

Why BI and AI want different things from storage

Lakehouse (BI tier): A columnar table format (Parquet) with ACID transactions, time travel, and schema evolution (Delta / Iceberg / Hudi). Optimized for analytical SQL queries that scan millions of rows across a few columns.

AI training does the opposite. It reads millions of rich, variable-shape samples in random order, each with images or video or embeddings, and streams them to GPUs at line rate. That access pattern is orthogonal to BI's "scan a few columns across millions of rows" workload, which is why it breaks.

Four things AI needs that BI lakehouses don't provide

If any of these hurt your team, the lakehouse tier is the wrong layer for the workload:

• Streaming tensor batches: Training loops want shuffled, batched, multi-tensor samples streamed to GPUs, not row groups decoded by an analytics engine.
• Small-file handling at scale: A single video dataset is millions of clips. Parquet-on-object-store falls over; you need chunking designed for this shape.
• Dataset versioning: Delta time travel gives snapshots. AI wants Git-like branches and diffs so teams can rerun experiments on previous label revisions.
• Hybrid query (vector + scalar + text): Agent retrieval and curation need one query plan across semantic similarity, filters, and full-text, not three external systems glued together.

BI lakehouse vs an AI data plane

Side-by-side across the operations that actually matter at training and retrieval time.

Reference: BI lakehouse + AI data plane side-by-side

The fix is not to migrate off the lakehouse. It is to stop forcing AI through it.

            ┌─► Delta / Iceberg ─► BI dashboards, SQL, Tableau
Raw data ──┤
            └─► Deeplake ──► GPU training (PyTorch / JAX)
                          ──► Agent retrieval (hybrid vector)
                          ──► Labeling + curation workflows

The BI tier keeps its job. The AI tier owns tensors, streaming, versioning, and vector queries. Teams that split the two this way stop paying latency tax on every training run and stop duplicating data into ad-hoc vector stores.

Add Deeplake to an existing lakehouse

Deeplake reads from the same S3 / GCS / Azure buckets your lakehouse already uses.

1. Install

pip install deeplake

2. Materialize a Deeplake dataset from S3

deeplake.create('s3://my-bucket/ds').extend_from(parquet='s3://lake/images/')

3. Stream batches to training

for batch in ds.pytorch(batch_size=128, shuffle=True): train(batch)

What teams try first (and why it doesn't hold)

• Parquet + S3 URIs: Each batch fan-outs to N small GET requests. Training GPU utilization drops to 30–40% waiting on storage.
• Delta Lake for tensors: Delta gives ACID but no shape-aware chunking. A 4K video frame in a column is still a blob.
• Pinecone + Parquet: Two sources of truth that must stay consistent. They don't. Curation queries get stale.
• Export + copy pipeline: A nightly job that materializes training data from the lake. Slow, expensive, and always a day behind reality.

FAQ

Should I rip out Delta or Iceberg?

No. Keep them for BI. They're good at the workload they were built for. Deeplake runs alongside for AI workloads that the lakehouse tier was never built to serve.

Can Deeplake read from the same S3 bucket as my lakehouse?

Yes. Deeplake stores chunked tensors as objects in the same bucket you already own. No new storage system to provision or back up.

Where does this help most?

Any team training on images, video, audio, 3D, or multimodal data, and any team doing retrieval for agents over unstructured content. Pure tabular ML on small data is fine on the lakehouse.

Is there a managed option?

Yes. Activeloop's managed Deeplake runs the storage, replication, and query layer. You point it at a bucket and get a queryable AI dataset.

What's the migration cost?

Low. Deeplake reads Parquet and object-store URIs directly, so you don't move data, you point Deeplake at it and start streaming.

Does this work for LLM fine-tuning too?

Yes. Text tokens are a tensor like any other. Deeplake streams them into HuggingFace trainers with no staging step.

Citations

• Activeloop. Deeplake overview and benchmarks.
• Zaharia et al. Lakehouse: A New Generation of Open Platforms. CIDR 2021.
• Activeloop. Deeplake on GitHub.

Keep the lakehouse. Add the AI tier.

Deeplake runs alongside Delta / Iceberg / Hudi and takes over the AI workloads they weren't built for.

Try Deeplake