DataForge Platform
Alloy
DataForge's storage architecture: the layer model that scales cleanly, stays transparent, and enables customization where it matters.
Instead of every team designing their own pipeline patterns, Alloy provides a single, named layer model that every data pipeline follows, making platforms easier to build, operate, and extend over time.
Pipeline layers
Data Platforms Handle Volume.
They Fail on Chaos.
Most data platforms find it hard to scale because of architectural inconsistency rather than data volume.
As new pipelines are added, teams introduce new patterns, exceptions, and one-off designs: each one reasonable in isolation, and collectively a platform where every pipeline has to be operated, debugged, and changed on its own terms.
When each team defines its own architecture, every new requirement becomes a design exercise. Alloy was built for the opposite problem.
Custom Architecture
- Every pipeline designed differently
- Patterns multiply over time
- Onboarding requires tribal knowledge
- Rebuilds are expensive and disruptive
- Operational complexity grows faster than data volume
Alloy
- Single enforced architecture across every pipeline
- New pipelines inherit the model automatically
- Transparent, operable, and documented by default
- Rebuilds become incremental changes
- Complexity grows within the same workflows
What happens when every pipeline follows its own design
This is a real production data platform after years of custom pipeline development without a standardized architecture.
Each point is a code package someone built by hand.
Over time, a platform like this gets harder to change and harder to trust.
A Clear, Explicit Layered Architecture
DataForge enforces a single, named layer model, Alloy, that every data pipeline follows.
Each layer has a clear purpose, and the boundaries between layers are explicit, so teams can understand and operate the platform directly from that structure.
The Alloy refinement process
Alloy has no hidden layers, implicit stages or shortcuts.
Every pipeline moves through the same explicit architecture.
ORE
Raw source data enters the platform in its original form, providing a consistent and observable starting point.
MINERAL
Changes are detected and isolated so incremental processing is the default from the start.
ALLOY
Data is enriched incrementally within a structured, repeatable transformation layer.
INGOT
Data is refined and consolidated into durable, reusable representations.
PRODUCT
Final outputs are delivered in a form optimized for analytics, applications, and downstream systems.
Incremental by Design
Most platforms treat incremental processing as a performance optimization added later.
In Alloy, it is a foundational behavior: built into the architecture from the first layer so pipelines reprocess only the data that has changed.
Ingest
Raw data arrives in ORE exactly as received from the source, at full fidelity, before any transformation or assumption is applied.
Detect
Mineral isolates only the records that are new or changed, so the downstream enrichment step only ever processes the incremental batch.
Enrich
Alloy applies business logic, rules, relations, and validations, only to the changed records. The result merges into a hub table that is always current.
Predictable data movement
Data moves through the platform using consistent execution patterns, eliminating one-off workflows and hidden dependencies.
Incremental by default
Change detection runs at the Mineral layer: only new and changed records flow through enrichment, and unchanged records stay as they are.
Transparent operations
Because execution follows a known structure, teams can reason about pipeline behavior, troubleshoot issues, and make changes directly.
Designed to scale on the same model
As new pipelines are added, they inherit the same execution model, preventing complexity from compounding over time.
Declarative Logic, Embedded in the Platform
Ember is the declarative knowledge layer of DataForge. It defines how data should be shaped, validated, and interpreted in a way that is reusable, consistent, and independent of any single pipeline.
By separating logic from execution, Ember allows organizations to scale their data platforms with each transformation defined once and reused across pipelines.
- Logic defined once: Transformations and rules are expressed declaratively, allowing a single definition of the logic to be reused, unchanged, across many pipelines.
- Column-level precision: Logic is defined at the level of individual data attributes rather than entire tables, enabling fine-grained control while keeping pipelines simple and predictable.
- Organizational knowledge, captured: Ember encodes shared understanding about data meaning and behavior, turning business logic into a durable asset instead of tribal knowledge embedded in code.
- Guardrails for automation: Because logic is explicit and structured, Ember provides a reliable, predictable foundation for automation and AI-assisted workflows.
How Ember drives the Alloy pipeline
Pipeline Execution
Customization, Where It Belongs
DataForge handles the vast majority of data integration and transformation needs using its enforced architecture and declarative logic.
For the cases that fall outside those patterns, DataForge provides carefully defined extension points where custom logic can be introduced while the surrounding architecture stays intact.
Customization is explicit
Custom logic is only introduced in designated parts of the pipeline, preventing hidden behavior and one-off patterns from spreading across the platform.
The architecture remains intact
Even when custom processes are required, they operate within Alloy's enforced layer model and execution structure.
Declarative first, code when necessary
Automated and declarative tools are the default path. Custom code is available for exceptional cases.
Your code stays standard Spark
Extension points accept any function returning a Spark DataFrame. Existing code fits with minimal changes, because a plain function is all the extension point needs.
The vast majority of DataForge implementations use only the automated and declarative tools.
Ad-hoc custom code vs. DataForge SDK
Built for Automation
Alloy's enforced architecture and Ember's declarative logic create a data platform with explicit structure and governed behavior. Pipelines follow known patterns. Logic is reusable and governed. Execution is consistent and observable.
This foundation is what makes AI-assisted automation practical. Because the platform behaves consistently, a conversational AI interface can reason about change, understand intent, and safely propose or apply updates, without introducing hallucinations or fragile workflows.
AI works when the system underneath it is designed to be understood.
Talos is DataForge's AI-powered conversational interface that translates natural language into action, operating within the guardrails defined by Alloy and Ember.
The DataForge Platform
DataForge's storage architecture: a consistent, layered pipeline model across every data source, so platforms stay operable as they grow.
Defines transformations, rules, and validations declaratively: reusable logic defined in the platform itself.
Translates natural language into pipeline actions safely, within the guardrails Alloy and Ember define.
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.