Datachain

Petabytes stored. No shared understanding.Object storage accumulates artifacts - not context. Here's what that costs you.Same work, repeated.Features extracted. Embeddings computed. No persistent record. Work starts over.Tribal knowledge.Context lives in Slack, notebooks, and people's heads. When they're unavailable, progress stops.Agents operating blindly.No catalog to search. No lineage to inspect. No versioned state to reuse. Just hallucinated pipelines.Empowering startups to Fortune 500 companiesSix lines. Context included.No SQL. No ETL. No data movement. Just Python.import datachain as dc ( dc.read_storage("s3://acme-robots/runs/**/*.mp4", type="video") .filter(dc.C("file.size") > 1000) .settings(parallel=8, prefetch=5, workers=150) .map(obstacles=detect_obstacles) .save("obstacle_detections") )1Point at storageConnect to any S3, GCS, or Azure bucket. No data copying, no ingestion step.2Transform with PythonFilter, map, and enrich using plain Python - LLMs, CV models, or any function.3Save as a datasetAutomatically versioned, lineage tracked, fully queryable.Every operation deposits context - metadata, lineage, and versioned state.The same code that runs on your laptop runs on a 150-node cluster.DataChain handles the parallelism, async download, checkpointing, and lineage.What changes when your storage accumulates context.Impossible with raw object storage. Automatic with DataChain.Find any file. Without asking anyone.No more Slack archeology. Anyone on the team can search, filter, and trace data to its source.Agents operate on shared stateClaude Code and other tools stop hallucinating pipelines - they reuse real datasets instead of creating duplicates.Reproduce anything. Instantly.Every file and transformation is versioned. Debugging goes from days to minutes.One workspace. Everyone in sync.Shared operational memory for researchers, engineers, QA, and agents.Open source to start. Studio to scale.Same SDK. Same concepts. The difference is scale and collaboration.Open SourceFor individuals and small teams building pipelines over object storage.Python SDK for S3/GCS/AzurePydantic-native schemasDataset versioning & lineageLocal parallel executionLLM & ML model integrationApache 2.0 licensepip install datachainStudioFor teams that need shared operational memory across the organization.Everything in Open SourceWeb UI & dataset registryTeam collaboration & access controlDistributed cloud compute (BYOC)MCP server for IDE & agent accessEnterprise support & SLAsBook a DemoStart locally. Scale to shared operational memory when your team or data grows - no rewrite required.What our customers say“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.Yoni SvechinskyDirector of Research | brain.space“What surprised me was how easily researchers adopted DataChain - data tools are usually hard for non-engineers. What surprised me more was when hardware and QA started asking for access too.Sharon KohenPrincipal Data Engineering | brain.space“DataChain added real value to our workflows - versioned datasets, automated ETL, and MLOps, all in Python. If you need a data management layer on top of cloud storage, give it a try.Nikhilesh SaggereLead Engineer | Alps Alpine EuropeTrusted partners with global industry leadersYour data never leaves your cloud.Your CloudData stays in your S3/GCS/Azure bucketCompute runs in your VPC (BYOC)No data copying or egressYou control access and encryptionDataChainMetadata and lineage indexed - never raw dataControl plane, not data planeRole-based access and audit logsSSO & SAML integrationComplianceSOC 2 Type II certifiedGDPR-ready data processingOn-prem deployment availableEnterprise security reviewsStorage without state is blind. Add the missing layer.Book a Callpip install datachain --- AllProductsCompanyDataChain BlogFind here DataChain news, findings, interesting reads, community takeaways, deep dive into machine learning workflows from data versioning and processing to model productionization.The Neuro-Data Bottleneck: Why Neuro-AI Interfacing Breaks the Modern Data StackNeural data like EEG and MRI is never 'finished' - it's meant to be revisited as new ideas and methods emerge. Yet most teams are stuck in a multi-stage ETL nightmare. Here's why the modern data stack fails the brain.Dmitry PetrovJan 23, 2026 • 5 min readParquet Is Great for Tables, Terrible for Video - Here's WhyParquet is great for tables, terrible for images and video. Here's why shoving heavy data into columnar formats is the wrong approach - and what we should build instead. Hint: it's not about the formats, it's about the metadata.Dmitry PetrovSep 03, 2025 • 5 min readFrom Big Data to Heavy Data: Rethinking the AI StackLLMs can finally interpret unstructured video, audio, and documents — but they can't do it alone. This post introduces the concept of heavy data and explores how modern teams build multimodal pipelines to turn it into AI-ready data.Dmitry PetrovJun 09, 2025 • 3 min readAs GenAI Fever Fades - Time to Prioritize Robust Engineering Over Overblown PromisesImproved Engineering and Data Management will be what carries GenAI into maturity Dmitry PetrovOct 23, 2024 • 3 min readScalable PDF Document Processing with DataChain and Unstructured.ioExtract and parse text from documents and create vector embeddings in a scalable and distributed way (and less than 70 lines of code). Tibor MachSep 30, 2024 • 7 min readPost-modern AI Data StackHow and Why Generative AI will change the modern data stack. Daniel KharitonovSep 24, 2024 • 7 min readYou Do the Math: Fine Tuning Multimodal Models (CLIP) to Match Cartoon Images to Joke CaptionsLearn how to fine tune multimodal models like CLIP to match images to text captions. Dave BerenbaumSep 12, 2024 • 9 min readOlder postsStorage without state is blind. Add the missing layer.Book a Callpip install datachain --- Ready to Transform Your Data Strategy?Contact our sales team to explore how DataChain can solve your specific use cases. We'll help you understand how to work with images, video, audio, PDFs, and embeddings at scale—directly in your cloud storage.Personalized demos and consultationsExplore use cases for your businessTechnical guidance and supportCustom solutions and pricingContact SalesName *Company Email *Role *Area of InterestSelect your area of interestMessage *Contact SalesStorage without state is blind. Add the missing layer.Book a Callpip install datachain --- Dmitry PetrovCo-Founder & CEOSerial founder with deep expertise in ML infrastructure. Built DVC (Data Version Control) at Iterative AI - the open-source standard for ML data versioning with 15K GitHub stars and 20M+ downloads, adopted by Fortune 500 companies and acquired by LakeFS.Former Data Scientist at Microsoft (Bing)PhD in Computer ScienceSpeaker at GitHub Universe, PyCon, MLOps World, Open Source SummitIvan ShchekleinCo-Founder & CTO20+ years in database systems and distributed computing. Co-founded Iterative AI and led the engineering behind DVC.Co-founded The Tweeted Times (acquired by Yandex)Published research on Databases at ACM SIGMOD.Mic LeeGo-To-MarketA seasoned sales leader with 15+ years driving go-to-market strategy, Mic bridges cutting-edge AI technology with real business outcomes.Former Sales Leader at Weights & BiasesDeep expertise in aligning technical products with customer business goalsStorage without state is blind. Add the missing layer.Book a Callpip install datachain