From Object Store Chaos to Data Context
❌ Raw Files Have No Context
- Data schemas are implicit. Meaning of data lives in Python code, not S3/GCS store.
- People and agents can't see the full picture. There's no unified view of datasets.
- Just figuring out what data exists requires rescanning storage and rerunning code.
Context is fragmented across storage, code, and people.
✅ Data Context Built at Cluster Scale
- Explicit, Pydantic-native schemas for signals, embeddings, and model outputs.
- A unified dataset registry with lineage, dependencies, and visibility.
- Data checkpoints - resuming compute without wasting compute and tokens.
Context becomes explicit, versioned, and computed at scale.
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See what DataChain can do
Turn raw files into clean, AI-ready data
Apply LLMs and ML models to extract insights from videos, PDFs, audio, and other unstructured data types. Effortlessly organize it into ETL processes.
Reproduce and data lineage
Capture full lineage of code, data, and parameters, enabling dataset reproduction and supplying code agents with context required for high-quality code generation.
Large-Scale Data Processing on Your Own Cloud
Scale to 25-1000+ machines in your own VPC using our BYOC model. Async downloading and distributed compute make multimodal processing extremely fast.