DatriseAI-first ETL

Lemlist Google BigQuery

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

How Datrise loads Lemlist into Google BigQuery

Datrise syncs Lemlist'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

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Lemlist entities map to Google BigQuery

Lemlist entityGoogle BigQuery objectNotes
recordslemlist_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventslemlist_eventsTIMESTAMP events
configuration objectslemlist_configuration_objectsid PK · linked to lemlist_records

FAQ

How does Datrise handle Lemlist'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 Lemlist 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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