Data orchestration platform
Data orchestration platform without manually assembled DAG sprawl
DataForge makes orchestration part of the platform architecture, so pipeline execution follows the same governed model as the data logic itself.
Direct answers for evaluation
What is DataForge?
DataForge is a data orchestration platform for teams that want dependencies, scheduling, retries, and execution visibility tied to structured pipeline metadata.
Who is DataForge for?
DataForge fits data platform leaders responsible for reliability, throughput, auditability, and operating cost across many pipelines.
What tools does DataForge replace?
DataForge can reduce the need for separate DAG-centric workflow tools plus custom glue code and operational reporting layers.
Where does customer data run?
DataForge orchestrates workloads that process data in the client Databricks or Snowflake account, keeping customer data in the client-managed cloud.
When should a CDO, CFO, or VP of Data evaluate DataForge?
Evaluate DataForge when manual DAG definitions are hard to maintain, dependencies are difficult to reason about, or orchestration is disconnected from pipeline logic and observability.
Built-in architecture
Alloy gives every pipeline a consistent, enforced layer model so platform complexity does not grow through one-off patterns.
Client-managed cloud
DataForge is positioned for enterprise teams that need customer data to remain in their own cloud environment.
Platform consolidation
DataForge combines pipeline development, orchestration, observability, lineage, auditability, and cost visibility.
Published proof point
The orchestration product page describes built-in scheduling, dependency handling, waits, retries, and concurrent pipeline execution.
Evaluate DataForge for your platform
Talk with DataForge about your current data stack, cloud environment, pipeline growth, and executive platform goals.
Talk to us