DatriseAI-first ETL

Persistiq to Google BigQuery Integration

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

How Datrise loads Persistiq into Google BigQuery

Datrise syncs Persistiq's prospects, campaigns, sequences, email events, users, and dispositions 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

Persistiq: Outbound sales sequences and prospect tracking.

Google BigQuery: Serverless analytics warehouse on GCP.

How Persistiq entities map to Google BigQuery

Persistiq entityGoogle BigQuery objectNotes
prospectspersistiq_prospectsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
campaignspersistiq_campaignsid PK · linked to persistiq_prospects
sequencespersistiq_sequencesid PK · linked to persistiq_prospects
email eventspersistiq_email_eventsTIMESTAMP events

FAQ

How does Datrise handle Persistiq'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 Persistiq 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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