Source connector

Looker connector

Looker integration for databases, warehouses and BI tools

BI platform with a modeled semantic layer. Datrise captures Looker entities such as looks, dashboards, explores, queries, users and scheduled plans, then lands normalized and typed models for warehouse reporting, attribution, and AI workflows.

The Looker API 4.0 authenticates with API key, incremental runs key on updated_at.

Of the 6 objects, 6 sync as upserts keyed on the primary key and 0 as append-only streams, so each incremental run moves only what changed since the last watermark. Every object lands in its own table (for example `looker_looks`) with a schema derived from the Looker API rather than a flattened CSV, in any of the 43 supported destinations.

Datrise is in early access: pick a destination below to open the Looker pipeline page, and join the waitlist to get the Looker connector with guided onboarding.

Incremental syncSchema-aware mapping43 destinations available

What Looker data Datrise extracts

Each object lands in its own typed table. Sync mode is derived from the object: history-style objects append, everything else upserts.

Object	Landing table	Sync mode
looks	looker_looks	Upsert by primary key, incremental on updated-at
dashboards	looker_dashboards	Upsert by primary key, incremental on updated-at
explores	looker_explores	Upsert by primary key, incremental on updated-at
queries	looker_queries	Upsert by primary key, incremental on updated-at
users	looker_users	Upsert by primary key, incremental on updated-at
scheduled plans	looker_scheduled_plans	Upsert by primary key, incremental on updated-at

Looker API: setup facts for the connector

Verified against the vendor documentation on 2026-09-02. Unknown values are left blank rather than guessed.

API	Looker API 4.0
Authentication	API key — A user's `client_id`/`client_secret` API3 key pair is POSTed to `/api/4.0/login` to obtain a bearer `access_token` (default 3600s TTL) sent as `Authorization: token <token>` on every subsequent call.
Pagination	offset — List/query endpoints (e.g. `run_query`, `search_looks`) use `limit`/`offset` parameters; `limit=-1` requests the full unlimited result set where the query and delivery method support streaming.
Rate limit	Not published
Incremental cursor	updated_at — Content-metadata endpoints (Look, Dashboard, User) expose an `updated_at` timestamp field for change detection; there is no platform-wide incremental-query cursor for arbitrary Explore data itself.
Webhooks	No
Bulk export	Yes — Scheduled deliveries / streamed unlimited query results to S3, SFTP, or webhook destinations
Deletes detectable	Yes — Looks and Dashboards are soft-deleted (a `deleted` boolean flips to `true`) before landing in Trash, so `search_looks`/`search_dashboards` with `deleted: true` surfaces recently removed content ahead of a permanent `delete_look`/`delete_dashboard` purge.

Official API reference

What bites when replicating Looker

Query results rendered through the UI (Explores, Looks, dashboards, SQL Runner) are capped at 5,000 rows; only an explicit API call with `limit=-1` plus the `download_without_limit` permission bypasses that cap, and PDF/PNG delivery formats stay capped at 5,000 regardless.
Content deleted through the UI is only soft-deleted (`deleted: true`, recoverable from Trash); a connector that treats `delete_dashboard`/`delete_look` as the only deletion signal will miss everything sitting in Trash until it's emptied.
The bearer `access_token` from `/api/4.0/login` expires after 3,600 seconds by default, so long-running extraction jobs must re-authenticate mid-run rather than caching a single token.
`limit=-1` unlimited results require the query to be able to stream to the chosen delivery method; requesting unlimited rows on a non-streamable format (e.g. PDF/PNG) silently falls back to the standard row cap instead of erroring.

Compare this against every other documented API

Popular destinations for Looker

Choose a destination below to open the ready pipeline page.

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Airtable
Relational spreadsheet destination for ops and go-to-market teams.
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Amazon Athena
Serverless SQL over S3 data lake tables.
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Amazon DynamoDB
Serverless key-value and document store on AWS.
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Amazon QuickSight
AWS serverless BI with SPICE and embedded analytics.
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Amazon Redshift
AWS petabyte-scale warehouse with Spectrum.
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Amazon S3 Data Lake
Object storage landing zone for parquet and snapshots.
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Apache Superset
Open-source BI for SQL exploration, charts, and dashboard publishing.
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Azure Data Lake Storage
ADLS Gen2 object storage for analytics workloads.
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Azure Synapse
Microsoft analytics workspace with SQL pools.
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Looker connector FAQ

What Looker data does the connector extract?: 6 objects: looks, dashboards, explores, queries, users and scheduled plans. Each lands in its own table with types preserved; history-style objects (0) load as append-only streams.
Is the Looker integration incremental?: Yes. After the initial full load, every run reads only records changed since the last watermark (updated-at or event timestamp) and applies an upsert or an append depending on the object.
Where can I load Looker data?: Into 43 destinations: relational databases, cloud warehouses, data lakes, spreadsheets and BI tools. Each Looker → destination pair has its own page with the object-to-table mapping.

Related sources

Other sources in the same category, each with its own connector page.

Early access

Launch your Looker pipeline with guided onboarding

Join the waitlist to get priority access for Looker integrations with guided setup, schema-aware mapping, and production-grade incremental sync.