Firebolt to Looker Integration
AI-first ETL from Firebolt into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Firebolt into Looker
Datrise syncs Firebolt's databases, tables, aggregating indexes, query jobs, table schemas, and partitions into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.
Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.
Ideal for governed, version-controlled BI on a warehouse.
Endpoints
Firebolt: Cloud data warehouse tuned for fast analytics.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Firebolt entities map to Looker
| Firebolt entity | Looker object | Notes |
|---|---|---|
| databases | firebolt_databases | id PK · custom fields → flattened columns (nested fields expanded for modeling) |
| tables | firebolt_tables | id PK · linked to firebolt_databases |
| aggregating indexes | firebolt_aggregating_indexes | id PK · linked to firebolt_databases |
| query jobs | firebolt_query_jobs | id PK · linked to firebolt_databases |
FAQ
How does Datrise handle Firebolt's custom fields in Looker?
Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.
How does the Firebolt to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
Related pipelines
More destinations for Firebolt
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