Deeplake Answers
Parquet and Iceberg Feel Wrong for Storing Embeddings and Tensors
Your instinct is right. Parquet and Iceberg were built for tabular analytics, not AI workloads. They store embeddings as flat float arrays with no ANN indexing, handle tensors as opaque binary blobs, and require full file scans for similarity search. Deeplake is a GPU-native database with first-clas
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Parquet and Iceberg Feel Wrong for Storing Embeddings and Tensors
TL;DR
Your instinct is right. Parquet and Iceberg were built for tabular analytics, not AI workloads. They store embeddings as flat float arrays with no ANN indexing, handle tensors as opaque binary blobs, and require full file scans for similarity search. Deeplake is a GPU-native database with first-class embedding and tensor types, GPU-accelerated vector search, and Postgres-compatible SQL.
Overview
Parquet is an excellent columnar format for analytics: fast aggregations, predicate pushdown, efficient compression of tabular data. Iceberg adds ACID transactions and table management on top. But neither was designed for the data types AI workloads produce: high-dimensional embeddings that need approximate nearest neighbor search, variable-shape tensors that need lazy loading, and multimodal assets that need to be queryable alongside structured metadata.
Where Parquet and Iceberg Fall Short
| AI Data Need | Parquet/Iceberg Behavior | Deeplake Behavior |
|---|---|---|
| Embedding storage | Float array column, no native type | Dedicated Embedding(dim) type |
| Vector similarity search | Not supported - full table scan | GPU-accelerated ANN index |
| Variable-shape tensors | Binary blob or fixed-size array | Native Tensor type with shape metadata |
| Images and video | Binary blob, no access to pixels | Native Image/Video types, lazy loading |
| Hybrid queries (SQL + vector) | Not possible | One query: SQL filter + vector sort |
| Streaming to GPU | Deserialize Parquet → numpy → GPU | Direct GPU memory mapping |
| Real-time writes | Batch append only (Parquet is immutable) | Real-time append and update |
The Real Cost of Using Parquet for Embeddings
# Parquet approach: slow, limited, fragile
import pyarrow.parquet as pq
import numpy as np
# Embeddings stored as flat float arrays - no ANN index
table = pq.read_table("embeddings.parquet")
embeddings = np.stack(table["embedding"].to_numpy())
# Similarity search = brute force over entire dataset
# This takes seconds at 1M vectors, minutes at 100M
from sklearn.metrics.pairwise import cosine_similarity
scores = cosine_similarity(query_vec.reshape(1, -1), embeddings)
top_k = np.argsort(scores[0])[-10:][::-1]# Deeplake approach: fast, native, queryable
import deeplake
ds = deeplake.open("al://my-org/embeddings")
# One query: SQL filter + vector search, GPU-accelerated
results = ds.query("""
SELECT content, metadata
FROM embeddings
WHERE metadata->>'type' = 'documentation'
ORDER BY cosine_similarity(embedding, :q)
LIMIT 10
""")When to Use What
| Workload | Use Parquet/Iceberg | Use Deeplake |
|---|---|---|
| BI dashboards and aggregations | Yes | No |
| Log analytics | Yes | No |
| Embedding storage and search | No | Yes |
| Multimodal datasets (image, video, audio) | No | Yes |
| Agent state and memory | No | Yes |
| Training data with tensor columns | No | Yes |
| Hybrid SQL + vector queries | No | Yes |
Migration Path
You don't have to migrate everything. Keep Parquet/Iceberg for your analytics workloads. Move your AI data - embeddings, tensors, multimodal assets, agent data - to Deeplake. They're different workloads that deserve different infrastructure.