For data and finance leaders scaling enterprise pipelines
Build enterprise data pipelines without stitching tools together
DataForge gives data teams a built-in architecture for reliable pipelines while keeping customer data in your Databricks or Snowflake environment.
The executive problem
Pipeline work slows down when every layer becomes a separate decision
As data teams scale, the operating model often fragments across ETL, orchestration, monitoring, lineage, quality, access, infrastructure, and cost tools. Each tool can be useful on its own, but together they create more integration work, more failure points, and more architecture to govern.
DataForge starts with the architecture and keeps it consistent. Leaders get a platform that can expand with the business while reducing the hidden cost of stitching the modern data stack together.
What DataForge replaces
Replace the stack your team keeps gluing together
DataForge is not only another pipeline builder. It gives data teams a shared operating layer for building, running, observing, and extending production pipelines without adding another tool for every problem.
ETL and ingestion tools
Build reliable movement and transformation patterns without turning every source or output into a custom engineering project.
Pipeline orchestration
Manage dependencies, retries, scheduling, and event-driven execution without maintaining another hand-authored DAG layer.
Data quality checks
Keep quality rules close to the pipeline definition so teams can catch issues before they become reporting or AI trust problems.
Observability and alerts
Track logs, alerts, runtime metadata, and operating signals from the same platform foundation that runs the pipelines.
Lineage and operational visibility
Understand what changed, why it changed, and how pipeline behavior affects downstream data products and business users.
Infrastructure Management
Reduce the platform work required to configure, operate, and tune the cloud infrastructure behind production pipelines.
How it works
Architecture is built in, not assembled later
DataForge uses a fixed, opinionated pipeline architecture so new work follows the same refinement flow by default. Teams describe the data logic and platform intent, while DataForge manages the structural pattern around it.
That means the platform can support new sources, domains, and output models without turning every initiative into a new architecture exercise.
Architecture
Built in, not assembled later
DataForge enforces a consistent platform architecture across pipelines, sources, domains, and teams. New work extends the platform instead of creating another one-off pattern.
Control
Data stays in your cloud
DataForge works with client-managed cloud environments and runs processing through your Databricks or Snowflake account, keeping platform control close to your enterprise boundary.
Velocity
Fast setup, fast extension
Declarative pipeline logic, templates, schema evolution, SDKs, and CI/CD support help teams add new sources and business logic without lengthy platform rebuilds.
Built for enterprise platform control
Your data stays in your cloud
DataForge coordinates pipelines, architecture, and operations while processing runs in your Databricks or Snowflake account. You keep control of the data plane; DataForge helps standardize how work gets built, run, and monitored.
Who it helps
Built for teams scaling beyond scripts and point tools
DataForge is for leaders who need data teams to move faster without creating another fragile layer of custom platform work.
CDOs
Standardize how data products are built, governed, and extended across teams.
VPs of Data
Reduce tool sprawl and spend less time diagnosing operational surprises.
Analytics leaders
Improve trust in the pipelines feeding dashboards, reporting, and AI use cases.
CFOs
Reduce duplicated tooling and make platform growth easier to understand and control.
When to evaluate DataForge
A better fit when the stack is becoming the bottleneck
DataForge is strongest when the business needs reliable pipeline delivery, but the team is spending too much time wiring tools, recreating patterns, and managing infrastructure choices.
Your team maintains separate tools for ingestion, orchestration, quality, monitoring, and infrastructure work.
Pipeline changes take too long because architecture gets reinvented for each new source, domain, or output.
You want Databricks or Snowflake to remain the execution layer instead of moving data into another vendor platform.
You need faster setup and extension without giving up enterprise control, auditability, or cloud ownership.
Proof in production
More platform leverage, less tool sprawl
DataForge has helped teams build thousands of pipelines with small engineering groups by standardizing the platform foundation and making extension the default motion.
6,800
pipelines built
85x
pipelines per developer per week
68
source systems for one customer
Solution 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 gives data engineering teams a structured platform for pipeline logic, orchestration, observability, and governance without forcing data outside the client 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.
For CDOs, CFOs, and data platform leaders
See how DataForge fits your stack
Talk with DataForge about your current platform, cloud environment, and pipeline growth plan. We will help map where architecture, orchestration, observability, infrastructure management, and cost control can be simplified.