DatriseAI-first ETL

Snapchat Ads Google BigQuery

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

How Datrise loads Snapchat Ads into Google BigQuery

Datrise syncs Snapchat Ads'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

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Snapchat Ads entities map to Google BigQuery

Snapchat Ads entityGoogle BigQuery objectNotes
recordssnapchat_ads_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventssnapchat_ads_eventsTIMESTAMP events
configuration objectssnapchat_ads_configuration_objectsid PK · linked to snapchat_ads_records

FAQ

How does Datrise handle Snapchat Ads'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 Snapchat Ads 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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