Ember
A prescriptive data catalog for scalable platforms
Ember defines how data is shaped, validated, and interpreted across the platform so logic remains consistent as pipeline complexity scales.
Ember is DataForge’s data catalog. It defines how data is shaped, validated, and interpreted across the platform rather than simply documenting what already exists.
Unlike traditional catalogs that observe data after the fact, Ember is prescriptive by design. It captures intent as declarative logic and applies it consistently across pipelines so meaning does not fragment as the platform grows.
At its core, Ember combines a structured knowledge model with reusable logic. This allows organizations to define data meaning once and apply it everywhere without duplicating transformations or embedding business rules deep inside pipeline code.
Catalogs That Observe Do Not Scale
Catalogs That Prescribe Do
Traditional data catalogs are built to document what already exists. They scan schemas, collect descriptions, and surface metadata after pipelines are deployed.
This works early. It breaks as pipelines multiply and logic diverges.
Ember was built for the opposite problem.
Traditional Catalogs
Observe data after pipelines are built
Document outcomes rather than intent
Track tables and assets, not logic
Drift as pipelines evolve independently
Require manual governance to stay accurate
Ember
Defines intent before pipelines run
Prescribes how data should behave
Encodes logic, validation, and meaning
Applies rules consistently across pipelines
Keeps meaning stable as complexity grows
Because Ember defines behavior rather than recording outcomes, it becomes part of how pipelines are built and operated, not a system consulted after the fact.
With Ember, the data platform has a true system of record.
An explicit source of truth for how data is defined and processed.
Where Definition Meets Execution
Ember does not stop at defining logic. It also captures how that logic executes in the real world.
Every declarative rule, transformation, and validation defined in Ember is linked to the pipelines that run it and the outcomes those pipelines produce. Execution details are not inferred later or stitched together from external systems. They are recorded alongside the logic itself.
This creates a living system of record that reflects both intent and reality.
What Ember captures
Declarative definitions for data shape, rules, and validation
How declarative logic executes within the single enforced pipeline architecture defined by Alloy
Execution metadata including timing, dependencies, and outcomes
Lineage that reflects actual transformations, not inferred guesses
Because definition and execution are stored together, Ember becomes the authoritative source for understanding how data behaves across the entire platform.
Understand Pipelines Without Reverse Engineering
In most data platforms, understanding pipeline behavior requires custom logging, external observability tools, and manual investigation.
In Ember, visibility is automatic and always accurate.
Because logic is declarative and execution is standardized, Ember captures operational detail as pipelines run. There is no need to instrument pipelines or reconstruct behavior from scattered logs.
What teams can see by default
Which rules and transformations ran
Where logic succeeded or failed
How long each step took
What downstream data was affected
This makes pipeline behavior understandable without reading code or tracing execution manually.
Why this matters
When teams can see what happened and why, they can debug faster, onboard new developers more easily, and make changes with confidence.
Operational clarity is not a feature. It is a consequence of a well-defined system.
Logic Has a Shape
Ember does not allow logic to be defined arbitrarily.
Every rule, transformation, and validation in Ember must conform to a small set of explicit, structured patterns. Logic is defined declaratively, attached to known entities, and scoped to specific stages of a fixed pipeline architecture.
Custom code is supported, but only in clearly defined extension points and only when necessary. It operates within the same structural boundaries rather than redefining how the pipeline works.
What this structure enforces
Logic must be defined declaratively, not embedded ad hoc in pipelines
Transformations attach to known entities and lifecycle stages
The unit of work is fixed and consistent across all pipelines
Custom code is allowed only in controlled, explicit locations
This constraint is intentional.
The result
Dependencies are always implicit
Orchestration is never manually defined
Pipeline design disappears as a concern
Complexity grows without multiplying workflows
Instead of every pipeline becoming its own orchestrated system, logic simply slots into a known structure.
Meaning remains consistent. Execution remains predictable.
This is a subtle advantage in small systems.
At scale, it is a fundamental shift.
Ember is not just a catalog that documents what happened.
It is a prescriptive definition layer that replaces pipeline design itself.
By fitting logic into a single enforced execution model, Ember removes the need to:
Design pipelines
Scope units of work, models, or packages
Define dependencies between steps
The architecture is predetermined.
Built for Automation at Scale
The same constraints that allow large engineering teams to scale without chaos are what make AI driven automation possible.
Ember works because it removes choice where choice creates fragmentation.
Logic has a fixed shape.
Execution follows a single model.
Definitions are explicit and shared.
This is what allows hundreds of engineers to contribute without creating hundreds of patterns.
Talos is DataForge’s AI control plane that translates natural language into Ember’s structured definitions.
Users describe what they want at a high level. Talos attempts to express that intent in Ember’s declarative structures. When something does not conform, Ember responds exactly as it would for a human developer by rejecting it.
There is no special path for AI. Ember is built to support both developers and AI from the ground-up.
LLMs are inherently non deterministic, which is unacceptable in enterprise data systems that drive high impact decisions. Ember enforces deterministic structures that make AI generated changes safe, reviewable, and repeatable.
Ember is the missing bridge between large language models and fully automated data pipelines.