SIGNLD vs LookML: who maintains the definition of revenue

LookML asks an analytics engineer to write and version the definition of revenue in code. SIGNLD resolves entities and metrics from your connected systems directly, so the question \"lookml vs knowledge graph\" comes down to who does the modelling work and how long you wait for the first answer.

TL;DR

LookML is Looker's modelling language, a version-controlled way to define views, explores, joins, and measures on top of a modelled warehouse. SIGNLD is a decision intelligence platform that connects read-only to 800+ business systems, resolves entities across them, and returns a Decision Brief with source citations and a confidence signal. Choose LookML if you have an analytics engineer and a warehouse that already reflects the business. Choose SIGNLD if you need an answer that spans systems nobody has modelled yet.

For the wider context, see our roundup of the best business knowledge graph platforms in 2026.

What is LookML actually built for?

LookML is a modelling language purpose-built for Looker, Google Cloud's business intelligence product. An analytics engineer writes views that map to tables, explores that define which joins are allowed, and measures that encode business logic such as how revenue is calculated. That file lives in Git, so changes go through review the same way application code does.

The genuine strength here is real. LookML gives a team explicit, reviewable, version-controlled control over metric logic, which matters when a finance team needs to prove that every dashboard uses the same definition of revenue and can show exactly when and why that definition changed. Once written, a measure like net revenue retention is consistent everywhere it is used across Looker.

Looker requires a modelled warehouse underneath it. It does not read raw operational systems directly; someone has to load and structure the data first, typically in BigQuery, Snowflake, or a similar warehouse, before LookML can define anything on top of it. Looker's pricing is quote-only through Google Cloud, based on usage and platform tier, so there is no public price list to compare against.

What is SIGNLD actually built for?

SIGNLD is a decision intelligence platform from Inzata Analytics. It connects read-only to the systems a company already runs, from ERP and CRM through the spreadsheets an operations team keeps updated, and resolves the same customer, invoice, or job across all of them inside a private Knowledge Graph. Learn how the underlying concepts fit together and which connectors are supported out of the box.

Nobody writes a LookML file first. A leader asks a plain-language question and gets a Decision Brief: the finding, evidence linked back to source records, a confidence score, and a recommended action. Inference runs on a single-tenant AWS Bedrock instance, using a private LLM powered by AWS Bedrock that is never trained on your data. You can connect the first system in 15 minutes and get a first answer in minutes, with no warehouse required.

SIGNLD's target user is a CEO, CFO, or COO at a 10 to 500 employee company with no dedicated data team, someone who needs an answer this week rather than a modelling roadmap.

Where the two genuinely differ

The core difference is where the definition of a metric lives and who is responsible for keeping it current. LookML puts that definition in code: a measure for revenue is a written formula in a view file, and if the business changes how it recognizes revenue, an analytics engineer edits that file, tests it, and ships the change through review.

SIGNLD does not have an equivalent file. It resolves entities and relationships automatically as each system connects, and a question about revenue draws on the underlying records directly rather than a stored measure. This trades the auditability of a single, hand-written formula for the ability to answer questions nobody anticipated when the model was built.

A second difference is scope. LookML only defines what is inside the modelled warehouse. If a number lives in a spreadsheet the finance team keeps locally, or a system that was never loaded into the warehouse, LookML has nothing to say about it. SIGNLD treats spreadsheets as first-class sources alongside connected systems, so a question that spans a CRM, an ERP, and a spreadsheet does not require adding a new table to the warehouse first.

A few structural differences worth naming directly:

LookML measures are written and maintained by an analytics engineer; SIGNLD resolves entities automatically as systems connect.
Looker requires a modelled warehouse underneath it; SIGNLD works without a data warehouse.
LookML changes go through Git review before they reach a dashboard; SIGNLD's answers cite source records directly, with no intermediate file to review.
Looker's audience is BI teams building governed dashboards; SIGNLD's audience is the executive asking the question.
LookML pricing is quote-only via Google Cloud; SIGNLD's plans are listed publicly.

	LookML	SIGNLD
what it models	views, explores, joins, and measures written by hand on top of a modelled warehouse	business entities, metrics, and their relationships resolved automatically as systems connect
who builds it	an analytics engineer writing and maintaining LookML files in Git	no one authors it, entity resolution runs as part of each read-only connection
time to first cross-system answer	weeks to months, after the warehouse is modelled and LookML measures are written	minutes after the relevant systems connect
source traceability	joins trace back to warehouse tables defined in the model	citations back to the source record in the originating system, with a confidence signal
where inference runs	no inference, Looker renders queries against the warehouse as defined in LookML	a single-tenant AWS Bedrock instance, private LLM powered by AWS Bedrock, never trained on your data
who it is for	BI and analytics engineering teams governing dashboards across a modelled warehouse	companies of 10 to 500 employees running 10 to 40+ systems with no dedicated data team

Who has to build the model?

In a Looker deployment, an analytics engineer writes the LookML: a view for each table, an explore that defines which joins are valid, and measures that encode calculations like gross margin or churned accounts. That work happens before a dashboard shows anything trustworthy, and it continues every time the business adds a new source or changes a definition.

In SIGNLD, nobody writes that file. Connecting a system triggers automatic entity resolution, matching the same customer record across a CRM and an accounting system without a person writing the join condition. A leader still sees the evidence behind an answer and can trace it to the source, but the ongoing maintenance work LookML assigns to an analytics engineer does not exist as a separate job in SIGNLD.

How long until you get a cross-system answer?

With LookML, time to a first cross-system answer depends on how mature the warehouse and the model already are. A team with a well-maintained LookML project can add a new explore in days. A team starting without a modelled warehouse is looking at a project measured in months, since the data has to be loaded and structured before a single measure can be written against it.

With SIGNLD, the first system connects in 15 minutes and the first answer follows in minutes, because entity resolution runs automatically as data streams in. A question that spans invoicing, sales pipeline, and a spreadsheet of manual adjustments does not wait on a warehouse project or a new LookML file.

Where LookML is the better choice

LookML is genuinely better when a team needs an explicit, auditable, single source of truth for a metric that many dashboards depend on. If a finance team has to prove to auditors exactly how net revenue retention is calculated, and show the Git history of every change to that formula, LookML's version-controlled measures do something SIGNLD does not attempt to replace.

Teams with an established analytics engineering practice, a mature warehouse, and BI users who need self-serve exploration across pre-approved joins get real value from LookML's governance model. That governance has a cost: someone has to write and keep maintaining the model as the business and its systems change, and a question outside the modelled explores returns nothing until the model is extended.

What running both looks like

Some organizations keep Looker and LookML as the governed reporting layer for metrics that feed board decks and regulatory filings, while SIGNLD answers the ad hoc, cross-system questions that come up between reporting cycles. LookML stays the system of record for the formulas that must never drift; SIGNLD answers the question a COO asks about a system that was never loaded into the warehouse.

Your data stays yours in SIGNLD through read-only connections, a single-tenant AWS Bedrock instance for the private LLM, and a policy of never training on customer data. See /security for specifics, and /concepts for how the Knowledge Graph and Topic model fit together.

Key takeaways

LookML is Looker's modelling language, a version-controlled way to define views, explores, joins, and measures on top of a modelled warehouse.
The core difference is where the definition of a metric lives and who is responsible for keeping it current.
With LookML, time to a first cross-system answer depends on how mature the warehouse and the model already are.
An analytics engineer or a BI developer, usually someone comfortable with SQL and Git.
If nobody on your team maintains a LookML project and you still need an answer that spans systems this week, start with the tool that resolves the model for you.

FAQ

Is LookML the same thing as a knowledge graph?

No. LookML is a modelling language for defining views, joins, and measures on top of a warehouse inside Looker. A knowledge graph, in the "lookml vs knowledge graph" sense SIGNLD builds, resolves entities and relationships across systems automatically, without a person writing a measure file first.

Do I need a data warehouse to use SIGNLD?

No. SIGNLD works without a data warehouse and connects read-only to operational systems and spreadsheets directly. LookML, by contrast, only defines models on top of data already loaded into a warehouse like BigQuery or Snowflake.

Who normally writes LookML?

An analytics engineer or a BI developer, usually someone comfortable with SQL and Git. LookML files are code, reviewed and versioned the way application code is, which is part of why they take specialist time to build and maintain.

What does Looker cost compared to SIGNLD?

Looker's pricing is quote-only through Google Cloud, based on usage and platform tier, so there is no public list price to compare directly. SIGNLD's current plans are listed on /pricing.

Can SIGNLD replace LookML for regulated financial reporting?

Not directly. LookML's version-controlled, auditable measures are built for exactly that use case. SIGNLD gives you a confidence signal and source evidence for a business decision, but a formula that must be provably unchanged for auditors belongs in a governed model like LookML.

Try SIGNLD free

If nobody on your team maintains a LookML project and you still need an answer that spans systems this week, start with the tool that resolves the model for you. Try SIGNLD free and connect a system in minutes, or Browse all articles for more on how the Knowledge Graph compares to other approaches.