Google BigQuery to GoodData Integration

How Datrise loads Google BigQuery into GoodData

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.

Ideal for embedded, multi-tenant analytics.

Endpoints

Google BigQuery: Serverless analytics warehouse on Google Cloud.

GoodData: Composable analytics platform with headless BI and embedded dashboards.

How Google BigQuery entities map to GoodData

Google BigQuery entity	GoodData object	Notes
datasets	`bigquery_datasets`	id PK · custom fields → flattened columns
tables	`bigquery_tables`	id PK · linked to bigquery_datasets
views	`bigquery_views`	id PK · linked to bigquery_datasets
query jobs	`bigquery_query_jobs`	id PK · linked to bigquery_datasets

FAQ

How does Datrise handle Google BigQuery's custom fields in GoodData?

Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native GoodData types.

How does the Google BigQuery to GoodData sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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