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.
Trusted by data leaders at companies like:
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 gives data teams a shared operating layer for building, running, observing, and extending production pipelines, in place of a separate tool for every problem.
ETL and ingestion tools
Build reliable movement and transformation patterns that every source and output can reuse.
Pipeline orchestration
Manage dependencies, retries, scheduling, and event-driven execution in the platform, in place of 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.
Who builds your business logic
DataForge provides a pre-built, fully integrated data platform and helps you find and build the business logic that turns your raw operational data into business insight that can drive decisions and AI-native solutions. You can buy the platform and the build from one company. Where you already have a partner doing the build, or want one, we work alongside them and review what they build.
You keep the decisions and the partner relationship.
The platform handles the tooling and the architecture, so a data-literate person working as part of a team can do the building. Defining what the target should mean remains human work.
How it works
Architecture is built in from the start
DataForge uses a fixed, opinionated pipeline architecture so new work follows the same refinement flow by default. Your team describes the data logic, and DataForge handles the layers, the processing order and the incremental strategy around it.
That means the platform can support new sources, domains, and output models within the architecture it already has.
DataForge 10.3 opens that governed architecture to external AI agents through the MCP server, whether pipeline processing runs on Databricks or Snowflake.
Architecture
Built in from the start
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 by extending the platform they already have.
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 on a standard platform, in place of 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 while keeping enterprise control, auditability, and cloud ownership.
Proof in production
More platform, 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 on one platform in place of separate ETL, orchestration, observability, lineage, and cost-control tools.
Data pipeline platform
Data pipeline platform for complex enterprise source systems
Build, extend, orchestrate, and observe enterprise data pipelines through one consistent architecture while processing data in your Databricks or Snowflake account.
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 that replaces 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 the Outcrop agent that runs inside your network, and merges it directly into governed tables that the transformation layer already consumes.
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.