DatriseAI-first ETL

Freshcaller to Google BigQuery Integration

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

How Datrise loads Freshcaller into Google BigQuery

Datrise syncs Freshcaller's calls, agents, queues, phone numbers, call metrics, and recordings 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

Freshcaller: Cloud call center for support and sales.

Google BigQuery: Serverless analytics warehouse on GCP.

How Freshcaller entities map to Google BigQuery

Freshcaller entityGoogle BigQuery objectNotes
callsfreshcaller_callsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
agentsfreshcaller_agentsid PK · linked to freshcaller_calls
queuesfreshcaller_queuesid PK · linked to freshcaller_calls
phone numbersfreshcaller_phone_numbersid PK · linked to freshcaller_calls

FAQ

How does Datrise handle Freshcaller'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 Freshcaller 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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