ETL and pipeline development
Define the intended result while DataForge applies a repeatable architecture for ingestion, change detection, enrichment, consolidation, and delivery.
Data engineering platform
The Data Engineering Platform That Comes Built, Not Assembled
DataForge is the only data platform with architecture embedded and automatically enforced. Your team defines what the data should become; DataForge standardizes how it gets there.
One platform for pipeline development, orchestration, observability, cataloging, and infrastructure management.
Trusted by data leaders at companies like
The Medallion problem
Medallion described the destination. It did not enforce the journey.
Bronze, silver, and gold gave data teams a useful shared idea. In production, those three layers become staging tables, temporary datasets, custom merge logic, and dependencies that differ from one pipeline to the next.
Tools such as ETL frameworks and workflow schedulers help teams build inside that outline, but they do not enforce the architecture. As the platform grows, the original promise gives way to more code, more exceptions, and more operational risk.
The DataForge difference
Alloy delivers what Medallion promised
Alloy is not a diagram or a convention your team must implement. It is the architecture DataForge automatically applies to every pipeline, across every source, domain, and team.
Batch and streaming follow the same five-stage refinement flow. Teams remain free to choose dimensional, flat, or operational outputs while DataForge keeps the path to those outputs consistent.
ORE Raw source data
→ MINERAL Change detection
→ ALLOY Enrichment
→ INGOT Consolidation
→ PRODUCT Final output
Replace the assembled stack
One platform instead of five disconnected tools
DataForge does more than put familiar features in one interface. The same metadata drives how pipelines are built, run, observed, understood, and governed, so context is not lost between tools.
Orchestration
Run dependencies, schedules, retries, and execution history from the same metadata that defines the pipeline.
Observability and data quality
Connect operational history, quality rules, alerts, and lineage directly to the pipelines they describe.
Catalog and governance
Keep data logic, configuration, lineage, and audit context together instead of reconstructing it across disconnected systems.
Infrastructure management
Standardize how workloads are built and operated across teams without moving data out of your cloud environment.
Your environment
Runs on your data, in your cloud
DataForge supports Databricks and Snowflake across AWS, Microsoft Azure, and Google Cloud. Processing runs in your provider account, where your data already lives.
Your team keeps control of the data plane while DataForge standardizes the architecture, pipeline logic, orchestration, and operations around it.
One system
Architecture, logic, and operations work together
Alloy, Ember, and Talos are connected parts of one data engineering platform, not separate products your team must integrate.
Alloy
The enforced architecture
Alloy gives every batch and streaming pipeline the same structural path, preventing each project from inventing its own layers and operating model.
Explore Alloy →
Ember
The catalog for data logic
Ember records column-level transformations, business rules, validation, lineage, and execution context as a shared system of record.
Explore Ember →
Talos
The architecture-aware AI control plane
Talos turns natural-language intent into structured platform changes constrained by Alloy and Ember, rather than generating arbitrary pipeline code.
Explore Talos →"Service Logic benefits tremendously from DataForge because it keeps our data integrations organized over time despite a complex and expanding landscape of systems. The platform accelerated our initial data transformation and is easy to maintain and enhance with minimal resources, allowing us to generate clean analytics that demonstrate the value of our business model, all without the need for a large team of in-house data experts."
Levi Reeves
VP of Integrations & FP&A
When to evaluate DataForge
Your platform should get easier to extend as it grows
DataForge is built for data engineering and platform leaders who need more delivery capacity without adding another tool, another custom framework, or another operating model.
- Every new pipeline introduces a different implementation pattern
- Changes require tracing undocumented code and dependencies
- ETL, orchestration, observability, and catalog tools lack shared context
- Platform standards depend on review rather than automatic enforcement
- Your team spends more time maintaining infrastructure than delivering data
Common evaluation questions
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, observability and data quality tools, catalogs, lineage systems, and custom infrastructure code.
Where does customer data run?
DataForge processes data in the customer Databricks or Snowflake account across AWS, Microsoft Azure, or Google Cloud.
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.
See how your platform would work in DataForge
Bring your current architecture, pipeline volume, and Databricks or Snowflake environment. We will show you where DataForge replaces custom work and disconnected tools.