Data Warehouse connector

The BigQuery data catalog your whole team can trust.

The Dawiso BigQuery connector turns your project into a searchable data catalog: every dataset, table, view and routine, with lineage to BI downstream.

Read the docs

Live connector Stable connector

BigQuery

Dawiso

Metadata-only · your data never leaves the source

Type: Serverless cloud data warehouse
Auth: Service Account Key (JSON)
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 Google BigQuery?

Google BigQuery is a serverless data warehouse on Google Cloud. There's no cluster to size and no machine to manage: you upload data, you query it in SQL, you pay for what you scan. Marketing teams, retailers and digital-native companies use it as their analytical home base, often alongside GA4 and Looker.

Knowledge Catalog (formerly Dataplex) covers what's inside BigQuery. What it doesn't cover is the Power BI report that consumes the table, the data product the business owns, and the policy that rides along. That's where the Dawiso BigQuery data catalog joins the picture: read-only, metadata-only, and cross-platform.

Architecture

How Dawiso connects to BigQuery

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

Source

BigQuery project

Datasets & tables
Views & materialized views
Routines & parameters
Policy tags & IAM

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: BigQuery REST API + INFORMATION_SCHEMA views
Authentication: GCP service account · JSON key · IAM-scoped role
Lineage: Object dependencies resolved from INFORMATION_SCHEMA; object-level lineage built from parsed view and routine SQL via Data Flow parsing on enterprise plans

Setup

Connect BigQuery in 4 steps

01

Create a custom IAM role

Add the BigQuery permissions Dawiso needs: datasets.get, tables.get/list/getIamPolicy, routines.get/list, jobs.create, readsessions.create/getData/update, projects.get/getIamPolicy.
02

Create a service account

Create a dedicated service account, attach the custom role, and generate a JSON Service Account Key. Store it somewhere safe; Google issues it only once.
03

Connect in Dawiso

Provide the project name and upload the JSON key. The connection is validated against BigQuery's INFORMATION_SCHEMA in seconds.
04

Run ingestion

Scheduled incremental sync keeps datasets, tables and routines current. Flow parsing resolves object-level lineage on enterprise plans.

Capabilities

What you get with the BigQuery connector

Dataset & table catalog

Every BigQuery dataset, table, view and routine is searchable, with column descriptions, types and owners.
Object-level lineage

Cross-platform lineage from a Looker dashboard through BigQuery views down to the raw GA4 export, via Data Flow parsing on enterprise plans.
Policy tag & IAM sync

BigQuery policy tags and dataset-level IAM bindings are pulled into the catalog so business sees who can read what, where.
PII classification

Classify a column once. Dawiso flags every BigQuery column carrying email, IBAN or government IDs across all datasets and projects.
Query & slot cost insight

INFORMATION_SCHEMA surfaces bytes-scanned and slot-ms per table, so 'which view costs us most' is finally a measurable question.
Ownership & certification

Mark tables as certified, deprecated or under review. The owner is visible directly in the catalog and stays alongside the BigQuery object.

Business value

Why teams turn on the BigQuery connector

−65%
Fewer 'which table?' pings

Analysts find the certified gold table in Dawiso instead of guessing whether to use the staging view or the raw export.
10×
Faster impact analysis

Before altering a BigQuery view, see exactly which views, Looker dashboards and ML features depend on it. Seconds, not days.
Audit-ready
GDPR & DORA evidence

Sensitive columns are classified once and the policy follows them through views, joins and downstream BI, with a full audit trail.

Ready to catalog your BigQuery?

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 BigQuery have a built-in data catalog?

Yes. BigQuery includes Dataplex (formerly Data Catalog) for assets inside Google Cloud. Dawiso adds the layer Dataplex does not cover: the Looker and BI reports, business glossary and policies that live outside BigQuery, with object-level lineage that spans all of them.

What is the difference between Dataplex and Dawiso?

Dataplex catalogs and governs data inside Google Cloud. Dawiso is a cross-platform data catalog: it reads BigQuery metadata read-only and connects it to the rest of your stack - other warehouses, BI tools, files and streaming - so lineage, ownership and classifications follow your data across tools, not just inside GCP.

What is a data catalog used for?

A data catalog makes every dataset, table and column discoverable, documented and trustworthy: who owns it, what it means, where it came from and whether it is certified. For BigQuery, Dawiso turns scattered datasets into one searchable catalog the whole business can use, not just the data team.

How do I connect BigQuery to Dawiso?

Create a read-only service account with metadata-viewer permissions, generate a JSON key, add it in Dawiso along with the projects you want to ingest, then run the scheduled sync. It is a four-step setup and no data leaves BigQuery.

What permissions does Dawiso need in BigQuery?

A custom IAM role bound to a dedicated service account, with these BigQuery permissions: datasets.get, datasets.getIamPolicy, tables.get/list/getIamPolicy, routines.get/list, jobs.create, readsessions.create/getData/update, and projects.get/getIamPolicy. Read-only end to end.

Does Dawiso copy our BigQuery data?

No. Dawiso queries INFORMATION_SCHEMA for metadata only. Row-level data stays inside BigQuery. Column profiling and sampling are opt-in per data source and never run automatically.

How is lineage built?

BigQuery does not expose native object-to-object lineage. Dawiso resolves it by parsing SQL from view and routine definitions via Data Flow parsing. Flow parsing is available on enterprise plans.

Where is the Service Account Key stored?

Encrypted at rest in your Dawiso tenant; for Private Connection deployments it never leaves your network. We recommend rotating the key on your standard GCP IAM rotation cadence.