SIGNLD vs Databricks: catalog vs Knowledge Graph

Databricks Unity Catalog governs permissions, lineage, and metadata over tables and columns across your lakehouse, but it does not model business relationships or decisions. SIGNLD's Knowledge Graph resolves entities like customers and orders across systems and answers a plain-language question directly, with sources attached.

TL;DR

Databricks is a data engineering and machine learning platform built on Delta Lake, with Unity Catalog handling governance, lineage, and permissions over tables and columns. Unity Catalog is metadata about data, not a model of business entities or decisions, and Databricks metric views need a data team to define them. SIGNLD connects to 800+ systems read-only, builds a Knowledge Graph that resolves entities like customers and invoices across systems automatically, and returns a ranked, traceable Decision Brief in plain language. Databricks needs engineers to build on it. SIGNLD is built for companies that do not have those engineers.

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

What is Databricks actually built for?

Databricks is a unified data engineering and machine learning platform, built around Delta Lake, an open storage format adding transactional reliability to cloud object storage. Data teams use it to ingest raw data, transform it through Spark jobs, train models, and manage tables at scale.

Unity Catalog is Databricks' governance layer. It tracks which tables and columns exist, who can access them, and how data moved through transformations, a capability called lineage. This matters for compliance and for teams that need to know what breaks if a table changes. But it describes structure and access, not meaning. It does not know that a "customer_id" column and a "client_ref" column refer to the same entity unless someone says so explicitly.

Databricks also has metric views, a way to define reusable metrics such as revenue or churn once, similar in spirit to a Power BI semantic model. On top sits Genie, Databricks' natural-language interface, which returns a SQL-backed answer drawn from tables a data team has curated and described with metric views.

Databricks assumes a skilled team exists to build pipelines, manage Delta Lake tables, register everything in Unity Catalog, and define the metric views Genie depends on. It is an engineering and machine learning platform with governance and a query layer on top.

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 to the spreadsheets an operations team maintains, and resolves those into a Knowledge Graph. Spreadsheets count as first-class sources, because operational truth at mid-market companies often never reaches a governed table.

A user asks a question in plain language and gets back a Decision Brief: a ranked answer, the entities and connections behind it, citations linking to the original source records, and a confidence signal. No Spark job needs to run first and no metric view needs to be authored before the first question is answerable. Connecting the first system takes about 15 minutes, and the first answer follows within minutes of that.

SIGNLD targets companies of 10 to 500 employees running 10 to 40 or more siloed systems, typically without a dedicated data engineering team, or with a small one that cannot keep pace with the questions a CEO, CFO, or COO is asking this week.

Where the two genuinely differ

The core difference is what each layer actually represents. Unity Catalog represents structure: which tables exist, who can touch them, and how data flowed between transformations. SIGNLD's Knowledge Graph represents entities and their relationships: this customer placed this order, which is tied to this invoice, which is overdue by this many days.

	Databricks (Unity Catalog)	SIGNLD
what it models	tables, columns, permissions, and lineage across the lakehouse	entities and relationships resolved automatically in a Knowledge Graph
who builds it	a data engineer or analytics engineer, via pipelines and metric views	forms automatically as systems connect, no one authors it manually
time to first cross-system answer	weeks to months, after ingestion, cataloging, and metric views exist	minutes after the relevant systems connect
source traceability	column-level lineage back through transformation jobs	traces to the source record in the originating system, with a confidence signal
where inference runs	configurable model providers depending on Databricks setup	a single-tenant AWS Bedrock instance, private LLM, no training on customer data
who it is for	organisations with an established data engineering and ML team	companies of 10 to 500 employees without a dedicated data team

A few concrete distinctions:

Unity Catalog's lineage shows where a column's data came from technically. SIGNLD's source traceability shows which business record supports a specific claim in an answer.
Databricks metric views need an engineer to define the logic in code before Genie can use it. SIGNLD's entity resolution happens automatically as systems connect.
Genie answers against tables already ingested and curated in the lakehouse. SIGNLD reads operational systems directly, including spreadsheets that would otherwise need a pipeline.
Databricks inference for Genie can run through several model providers depending on configuration. SIGNLD's private LLM powered by AWS Bedrock runs on a single-tenant instance, and we never train on your data.
Databricks pricing is consumption-based, billed per Databricks Unit, public but hard to predict without usage history. SIGNLD's plans are listed on pricing.

For a closer look at the natural-language layer specifically, see SIGNLD vs Databricks.

Who has to build the model?

In Databricks, a data engineer builds the ingestion pipeline, a data engineer or analytics engineer registers tables in Unity Catalog with the right permissions and descriptions, and someone, often the same person, writes the metric view logic that Genie will later query against. This is legitimate engineering work and it produces a durable, governed asset once done well. A company with an established platform team reuses this work across many pipelines.

In SIGNLD, the Knowledge Graph forms as systems connect, with entity resolution happening automatically rather than through a manually written join or metric definition. Nobody writes a metric view or a Unity Catalog grant statement to get a first answer. The person asking, typically an executive without an engineering background, does not need to know the schema of the underlying systems.

This gap is exactly where SIGNLD is aimed: companies with 10 to 40 systems and no engineer available to build and maintain a Databricks-grade catalog and metric layer, but with real cross-system questions that need answering this week rather than next quarter.

How long until you get a cross-system answer?

A typical Databricks path to a cross-system answer runs through several stages: ingest each source into Delta Lake, register tables in Unity Catalog, write the transformation logic that joins them, then define a metric view so Genie can query it. For an organisation building this from scratch, that sequence often takes weeks to a few months.

SIGNLD's timeline runs in minutes for the first connection and the first answer. Because entity resolution is part of connecting a system rather than a distinct engineering phase, a question spanning two systems, for example a CRM and a billing platform, can be answered the same day the second system connects.

This is not a knock on Databricks' speed once the platform is built. A mature Databricks deployment with well-maintained metric views and Genie configured answers new questions fast too. The difference is the setup cost paid before the first answer exists, versus the near-zero setup cost for each additional SIGNLD connection.

Where Databricks is the better choice

Databricks is the stronger choice for large-scale data engineering and machine learning. Training and serving models, running complex Spark transformations over terabytes of event data, and managing Delta Lake tables with strong transactional guarantees are jobs SIGNLD is not built to do and does not attempt. If a company's core work involves building predictive models, training on historical data at scale, or running heavy ETL across dozens of pipelines, Databricks is the mature, proven platform for that job.

Databricks also wins on lineage governance for a real data team. Unity Catalog's access control and column-level lineage matter for regulated industries where knowing what feeds a report is required, not a convenience. A company with an established data team and machine learning workloads should keep Databricks.

What running both looks like

Companies running Databricks do not need to give it up to use SIGNLD. Databricks remains the platform for engineered pipelines, model training, and governed lakehouse tables that a data team maintains. SIGNLD sits alongside it, reading the same operational systems plus the spreadsheets and SaaS tools that never got a pipeline built for them, and answers the ad hoc cross-system question an executive asks between board meetings.

Key takeaways

Databricks is a data engineering and machine learning platform built on Delta Lake, with Unity Catalog handling governance, lineage, and permissions over tables and columns.
The core difference is what each layer actually represents.
A typical Databricks path to a cross-system answer runs through several stages: ingest each source into Delta Lake, register tables in Unity Catalog, write the transformation logic that joins them, then define a metric view so Genie can query it.
Databricks bills consumption-based, per Databricks Unit used, a mechanism that is public but hard to estimate without usage history.
If Unity Catalog tells you where a column came from but not what your customers, orders, and invoices actually mean together, SIGNLD closes that gap without a pipeline build.

FAQ

Is Unity Catalog a knowledge graph?

No. Unity Catalog is a governance layer that tracks tables, columns, permissions, and lineage across a Databricks lakehouse. It describes structure and access to data, not modelled business entities such as customers or orders and the relationships between them, which is what a Knowledge Graph represents.

Does Databricks Genie need a data team to work well?

Yes, in most deployments. Genie answers questions against tables and metric views that a data engineering team has already ingested, cleaned, and defined. Answer quality depends directly on how complete and accurate those underlying metric views are.

Can SIGNLD replace Databricks?

Not for data engineering or machine learning. SIGNLD does not run Spark jobs, train models, or manage Delta Lake tables. It replaces the modelling step needed to answer a specific cross-system business question, which is a narrower job than Databricks' full platform scope.

How is Databricks priced compared to SIGNLD?

Databricks bills consumption-based, per Databricks Unit used, a mechanism that is public but hard to estimate without usage history. SIGNLD does not publish pricing figures. Current plans, including a free tier, are listed on pricing.

Where does SIGNLD's AI processing run?

SIGNLD's inference runs on a private LLM powered by AWS Bedrock, on a single-tenant instance dedicated to your account. Your data stays yours through read-only connections to source systems, and we never train models on your data.

Try SIGNLD free

If Unity Catalog tells you where a column came from but not what your customers, orders, and invoices actually mean together, SIGNLD closes that gap without a pipeline build. Try SIGNLD free and connect a system in minutes, or Browse all articles for more on graphs, catalogs, and semantic layers.

SIGNLD vs Databricks: Unity Catalog is a catalog, not a Knowledge Graph

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