5-10× faster EEG, MRI, and DICOM pipelines. Typed datasets, lineage, and distributed compute: from your notebook, your script, or your AI agent.
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Faster pipelines. At brain.space, typed datasets and distributed compute turn days of EEG processing into hours.
Faster debugging. Resume from checkpoints and trace every transformation, without restarting the whole pipeline.
From one notebook. Run familiar Python across your cloud infrastructure, without an extra framework.
In your cloud. Keep subject data in your storage and compute in your VPC, under your existing access controls.
Point at S3, GCS, Azure, Nebius, or on-prem. Files stay where they are; DataChain reads schemas, lineage, and processing history.
From a notebook, a script, or an AI agent. Dispatch to 1 to 1000+ machines in your VPC when scale demands it.
Every pipeline saves a typed dataset to the team's registry. The next session reads what exists instead of re-deriving from raw bytes.
Collaboration
Every pipeline adds a named, versioned, lineage-traced dataset to your shared layer. Researchers, data engineers, hardware, QA, and external partners can all build on the same work.
Visualization
Inspect DICOM, NIfTI, and EEG traces inline in Studio. Each file links to its dataset version, schema, and source code: scans and code together, with zero exports and one source of truth.
Distributed compute
Give notebooks, scripts, and agents access to cloud infrastructure. Dispatch jobs across hundreds of machines in your VPC, using familiar workflows and on-demand compute.
AI agents
Make Claude Code, Cursor, and Codex productive on data. Beyond raw files, DataChain gives them a typed catalog, lineage, and clusters to turn each pipeline into reusable team results.
We realized we were solving a problem we shouldn't be solving. With DataChain, what used to require data engineers is now handled seamlessly by researchers - and the whole team moved to the next level.
Testimonial 1 of 2: Yoni Svechinsky
Subject data never leaves your cloud account. Trace every transformation, preserve every version, and stay audit-ready by default.
SOC2-certified. Compute and storage stay in your AWS, GCP, or Azure account, under your IAM.
Follow every signal-to-feature transformation. Dataset versions stay immutable, with lineage attached.
Trace subject withdrawal across derived datasets. Propagate IRB amendments as new dataset versions.
brain.space · Customer story
brain.space turns EEG, neuroimaging, ECG, EDA, and eye-tracking signals into reusable datasets for Large Mental Models. Before DataChain, five researchers waited 1–2 days to debug pipelines and depended on a data engineer to run distributed compute.
The data-engineering bottleneck is gone. Researchers work directly with their datasets and compute.
Checkpoint recovery and immutable lineage replace the 1–2 day wait to trace and rerun a pipeline.
High-frequency EEG processing moves from days to hours, with typed datasets and distributed compute.
Compute runs across brain.space’s own AWS and Nebius GPU infrastructure. DataChain orchestrates without touching the bytes.
brain.space’s migration from Flyte + Spark + SQL. The same approach extends to Airflow and custom ETL on multimodal bio data.
| Capability | Flyte + Spark + SQL | DataChain |
|---|---|---|
| Multimodal data | Custom per-format models | Pydantic-typed biomedical data |
| Researcher access | Data engineer ticket | Notebook, script, or AI agent |
| Distributed compute | Steep learning curve | 1–1000+ machines from one Python session |
| Debugging | 1–2 days | Minutes, with checkpoint resume |
| Lineage | Manual or brittle | Automatic on every save |
| Delta updates | Reprocess everything | Touch only what changed |
| BYOC | Self-hosted infrastructure | Your cloud or on-prem; control plane only |
