DatriseAI-first ETL

Recurly Google BigQuery

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

How Datrise loads Recurly into Google BigQuery

Datrise syncs Recurly's subscriptions, invoices, plans, transactions, and dunning events 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

Recurly: Subscription management and recurring billing platform.

Google BigQuery: Serverless analytics warehouse on GCP.

How Recurly entities map to Google BigQuery

Recurly entityGoogle BigQuery objectNotes
subscriptionsrecurly_subscriptionsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
invoicesrecurly_invoicesid PK · linked to recurly_subscriptions
plansrecurly_plansid PK · linked to recurly_subscriptions
transactionsrecurly_transactionsid PK · linked to recurly_subscriptions

FAQ

How does Datrise handle Recurly'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 Recurly 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

Connect Recurly to Google BigQuery the easy way

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