The DAG is dead for data engineering
DataForge uses the software engineering concepts of inversion of control and event-driven architecture to automate data pipeline orchestration. Eliminate the need to define Directed Acyclic Graphs (DAGs) manually and let your functional code decide when and how to execute.
The next generation of data orchestration
Your transformation code is your orchestration code
Inversion of control combined with functional programming allows your transformation code to also define the order of operations required to process data correctly. No need to manually analyze your pipeline logic and data to determine optimal execution order.
Standardized stages for common tasks
Hyperparameters for data engineering
DataForge Cloud provides predefined and tune-able stages to simplify and generate code for the most common types of data processing. Just input basic configurations and DataForge will combine them with the live incoming data elements to generate efficient code and associated orchestration steps.
Built-in scheduling and dependency engine
Use the DataForge scheduling service, file watcher, REST API, or SDK to initialize processing, then the built-in dependency engine handles the rest. It tracks all processes and determines next steps, waits, and retries. Run thousands of concurrent pipelines in parallel and manual one-offs without worry.
Optimize cloud spend with dynamic clusters
DataForge Cloud provides an automated infrastructure management service combined with orchestration for Databricks customers. By using metadata as well as the most cost effective available cloud products, DataForge helps minimize spend and maximize performance.
DataForge Cloud
All-in-one web platform
DataForge Cloud is the fastest and most reliable way to deploy DataForge. Develop, orchestrate, operate, and audit functional code pipelines in an all-in-one web-based UI.
Start for freeDataForge Core
Open source CLI
DataForge Core is an open source command line tool that enables teams to write functional data transformation code following software engineering best practices and principles.
View on GitHubSolution guides
Evaluate DataForge by platform goal
Enterprise data platform
Enterprise data platform for governed analytics at scale
DataForge helps CDOs, CFOs, and data platform leaders scale analytics without assembling separate ETL, orchestration, observability, lineage, and cost-control tools.
Data pipeline platform
Data pipeline platform for complex enterprise source systems
DataForge helps data teams build, extend, orchestrate, and observe enterprise data pipelines while preserving a consistent architecture across every source and output.
Data engineering platform
Data engineering platform with architecture built in
DataForge is a data engineering platform with automatically enforced architecture, replacing separate ETL, orchestration, observability, catalog, and infrastructure tools while processing data in your cloud.
Data orchestration platform
Data orchestration platform without manually assembled DAG sprawl
DataForge orchestrates data pipelines from structured pipeline definitions, dependency metadata, scheduling, and execution history instead of manually maintained DAGs.
Data observability platform
Data observability platform with lineage, quality, audit, and cost context
DataForge observability ties code, orchestration, quality rules, alerts, lineage, audit trails, and cloud cost visibility back to the platform metadata.
Agentic data engineering
Agentic data engineering platform with save-time validation for AI agents
DataForge lets AI coding agents such as Claude Code and Codex build and operate governed data pipelines through an MCP server that validates every write at save.
Data replication
Data replication that ends inside the governed platform
DataForge replicates SQL Server change data through Outcrop, the DataForge agent that runs inside your network, and merges it directly into governed tables that the transformation layer already consumes.