DatriseAI-first ETL

Gridly Google BigQuery

AI-first ETL from Gridly into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Gridly into Google BigQuery

Datrise syncs Gridly's records, events, and configuration objects into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Gridly: SaaS or API data source for analytics and warehouse sync.

Google BigQuery: Serverless analytics warehouse on GCP.

How Gridly entities map to Google BigQuery

Gridly entityGoogle BigQuery objectNotes
recordsgridly_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventsgridly_eventsTIMESTAMP events
configuration objectsgridly_configuration_objectsid PK · linked to gridly_records

FAQ

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

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

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

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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