ETL / ELT connector

The dbt data catalog your whole team can trust.

The Dawiso dbt data catalog turns your dbt project into a searchable inventory: every model, test and snapshot, with model and source lineage into the warehouse.

Read the docs

Live connector Stable connector

dbt

Dawiso

Metadata-only · your data never leaves the source

Type: SQL transformation framework
Auth: dbt Cloud service-account token · Metadata Only permission
Sync: Scheduled, incremental
Direction: Read-only · metadata

First things first

What is a data connector?

Metadata-only Read-only access Incremental sync Cross-system lineage

A data connector is the bridge between a tool in your stack and the catalog that gives you a unified view of it. Once a connector is configured, it reaches into the source system on a schedule, reads out the metadata - schemas, tables, dashboards, jobs, ownership, lineage - and represents it inside the catalog. Your actual rows and values stay where they are.

Connectors are the reason a data catalog can answer questions like "which Power BI dashboard depends on this Snowflake table?" or "who owns the orders topic in Kafka?" - automatically, without anyone keeping a spreadsheet up to date.

Three properties separate a good connector from a brittle one: it should be read-only and safe, it should be incremental so a full re-scan isn't required for every refresh, and it should resolve lineage across system boundaries, not just inside one tool.

About the platform

What is dbt?

dbt (data build tool) is the SQL transformation framework that lets analytics engineers build, test and document data models directly in the warehouse. dbt Cloud exposes models, sources, tests and their relations through its Metadata API. Snowflake, BigQuery, Databricks and Redshift are the typical targets.

dbt Cloud's own Explorer covers models inside a dbt project. What it doesn't cover is the warehouse table the model writes to, the BI report consuming it, the data product the business owns, or the Airflow DAG that triggered the run. That's where the Dawiso dbt data catalog joins the picture: read-only, metadata-only, and cross-platform.

Architecture

How Dawiso connects to dbt

A small read-only role on the dbt side. The Dawiso scanner pulls metadata on a schedule. Everything ends up in your catalog, business-readable.

Source

dbt Cloud account

Projects & environments
Models, tests, snapshots, seeds
Sources & exposures
Jobs & run history

REST · JDBC

Dawiso scanner

Read-only metadata

Schema & object discovery
Dependency resolution
SQL flow parsing (optional)
Sampling on opt-in

Internal

Catalog

Dawiso platform

Searchable metadata
Lineage & ownership
Business glossary
Policy & classifications

Connection details

Protocol: dbt Cloud Metadata API (GraphQL)
Authentication: Service-account token with Metadata Only permission · regional API endpoint
Lineage: Model and source relations and lineage from the dbt Cloud Metadata API; cross-platform stitched with Snowflake, BigQuery, Databricks and Power BI sources

Setup

Connect dbt in 4 steps

01

Generate a service-account token

In dbt Cloud, open Your Profile > Service tokens > + Create service token. Assign the Metadata Only permission set. Save the token; dbt displays it once.
02

Pick your regional API endpoint

Choose your Metadata API endpoint: US Prod 1, US Prod 2, EMEA Europe or APAC Australia. The endpoint is per region; mismatched endpoints fail the connection test.
03

Connect in Dawiso

Provide the API URL, the service-account token and the dbt Cloud Account ID. Select projects, environments and jobs via regex. Production-only toggle filters to PROD environments.
04

Run ingestion

Scheduled incremental sync keeps models, tests and run history current. Model and source lineage rebuilds from the dbt Cloud Metadata API on every ingestion.

Capabilities

What you get with the dbt connector

Model & test catalog

Every dbt model, test, snapshot and seed is searchable, with materialization, owner, tags and the project it lives in.
Model & source lineage

Cross-platform lineage from a Power BI visual through dbt models down to the raw warehouse table, resolved from the dbt Cloud Metadata API.
Tests & contracts

dbt model contracts and test results sit next to the asset in the catalog, so 'is this table trustworthy today' has an answer.
PII classification

Classify a column once on a dbt model. Dawiso flags every downstream warehouse column, view and BI field carrying the same data.
Ownership & certification

Model owners and tags from dbt land in the catalog. Promote a model to certified, deprecate one, and the business sees it.
Jobs & freshness

Job run history and last-built timestamps surface inside the catalog, so analysts know whether the model is hours or days fresh.

Business value

Why teams turn on the dbt connector

−65%
Fewer 'which model?' pings

Analysts find the certified mart model in the catalog instead of pinging the analytics engineer to ask which staging model maps to revenue.
10×
Faster impact analysis

Before changing a dbt source, see exactly which downstream models, warehouse views and BI dashboards depend on it. Seconds, not days.
Audit-ready
Transformation traceability

Every model, test, owner and run is in the catalog with downstream warehouse and BI assets, so audit answers come from the platform, not Slack.

Ready to catalog your dbt?

Set up the connector in an afternoon. See your first lineage graph the same day.

Read the docs

Frequently asked questions

Still curious? Talk to our team ->

Does dbt have a data catalog?

dbt generates docs and a catalog, surfaced in dbt Explorer. Dawiso reads model, source and test metadata through the dbt Cloud Metadata API and merges it with warehouse and BI metadata, so model lineage extends across your whole stack, not just within dbt.

What is the difference between dbt catalog and docs?

dbt docs is the generated documentation site; the catalog is the metadata behind it. Dawiso reads model definitions, tests and lineage from the dbt Cloud Metadata API and combines them with the rest of your platform in one searchable catalog.

What permissions does Dawiso need in dbt?

A dbt Cloud service-account token with the Metadata Only permission set. The token reads project, model, test, snapshot, source, job and run metadata via the dbt Cloud Metadata API (GraphQL). Read-only end to end.

Does Dawiso copy our dbt SQL or data?

Dawiso ingests dbt metadata through the dbt Cloud Metadata API. Model and source relations drive lineage, but row-level data your dbt models build stays in the warehouse. Profiling is opt-in per data source.

How is dbt lineage built?

From the model and source relations exposed by the dbt Cloud Metadata API. Cross-platform lineage stitches dbt models with ingested Snowflake, BigQuery, Databricks and Power BI objects so the trace runs from BI visual to raw source.

Which dbt versions are supported?

The latest dbt Cloud version. Dawiso tracks the dbt Metadata API and is updated regularly. Requires a dbt Cloud account with a Developer license and admin permissions to generate the service-account token. dbt Core (CLI-only) ingestion is on the roadmap; contact Customer Success.