Reply.io to Google BigQuery Integration

How Datrise loads Reply.io into Google BigQuery

Datrise syncs Reply.io's campaigns, prospects, sequences, email events, calls, and replies 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

Reply.io: Multichannel sales engagement sequences.

Google BigQuery: Serverless analytics warehouse on GCP.

How Reply.io entities map to Google BigQuery

Reply.io entity	Google BigQuery object	Notes
campaigns	`reply_io_campaigns`	id PK · custom fields → JSON or nested/repeated (STRUCT) columns
prospects	`reply_io_prospects`	id PK · linked to reply_io_campaigns
sequences	`reply_io_sequences`	id PK · linked to reply_io_campaigns
email events	`reply_io_email_events`	TIMESTAMP events

FAQ

How does Datrise handle Reply.io'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 Reply.io 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

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