Data engineering platform
Data engineering platform with architecture built in
DataForge helps data engineering teams move from custom pipeline assembly to a structured operating model that is easier to build, audit, and extend.
Direct answers for evaluation
What is DataForge?
DataForge is a data engineering platform that combines declarative pipeline logic, enforced architecture, orchestration, observability, and governance.
Who is DataForge for?
DataForge fits VPs of Data, data engineering leaders, analytics engineering leaders, and platform teams that need more throughput without multiplying tool sprawl.
What tools does DataForge replace?
DataForge can reduce reliance on separate ETL frameworks, workflow schedulers, data quality monitors, lineage tools, and custom platform code.
Where does customer data run?
DataForge is designed for client-managed cloud deployments where processing occurs in the customer Databricks or Snowflake account.
When should a CDO, CFO, or VP of Data evaluate DataForge?
Evaluate DataForge when engineering capacity is constrained, platform standards are hard to enforce, or changes require expensive rebuilds across many pipelines.
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 site describes 85x pipelines per developer per week as a published productivity metric.
Evaluate DataForge for your platform
Talk with DataForge about your current data stack, cloud environment, pipeline growth, and executive platform goals.
Talk to us