Data-driven pipelines are automatically triggered based on detecting data changes to any repo.
Autoscaling and parallel processing built on Kubernetes for resource orchestration.
Easy to move your machine learning workflow from testing to production.
Fully integrated version control for your pipelines and data - roll back to previous pipelines as needed.
Freedom to use any language with code agnostic, containerized pipelines.
Immutable data lineage with data versioning and full transformation history.
Runs across all major cloud providers and on-premises installations.
Enterprise support and documentation from Pachyderm's Customer Engineering Team
Automatically trigger pipelines based on data changes.
Orchestrate batch or real-time data pipelines from any data source.
Diff-based automation just like a CI/CD system but for data.
Versioned data is automatically deduplicated.
Intelligently process only modified data and dependencies.
Track every change to your data and pipelines automatically as you work.
Autoscale jobs up and down based on resource demand.
Automatically parallelized processing of large data sets.
Full process visibility and monitoring using Kubernetes-native tools.
Ensure reproducibility and compliance via immutable data lineage and data versioning for any type of data.
Increase team efficiency and collaboration via git-like structure of commits, branches, and data repositories.
All data and pipeline code is versioned providing an immutable record for all activities and assets.
Track any result all the way back to its raw input.
Full versioning for metadata including all analysis, parameters, artifacts, models, and intermediate results.
Automatic and intelligent versioning of even the largest data sets of unstructured and structured data.
Git-like structure enables effective team collaboration.
Diff between two commits of data to debug data, code, or model failures more efficiently.
Deliver reliable results optimizing resource utilization and maximizing developer efficiency.
Run complex data pipelines with sophisticated data transformations with auto scaling and parallelism.
Deduplication of data and code saves infrastructure costs.
Leverage your infrastructure investments and run on your existing cloud or on-premises infrastructure.
Run again any type, size, or scale of data in both batch or real-time pipelines.
Support effective team collaboration through git-like structure of commits.
Robust tools for deploying and administering Pachyderm at scale across different teams in your organization.
Centralized licensing and administration of all clusters.
Authentication against any OIDC provider.
Role based access control (RBAC) support for governance and data privacy.