DatriseAI-first ETL

Dixa Google BigQuery

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

How Datrise loads Dixa into Google BigQuery

Datrise syncs Dixa's conversations, agents, customers, tags, and resolution metrics 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

Dixa: Customer service platform for conversations across channels.

Google BigQuery: Serverless analytics warehouse on GCP.

How Dixa entities map to Google BigQuery

Dixa entityGoogle BigQuery objectNotes
conversationsdixa_conversationsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
agentsdixa_agentsid PK · linked to dixa_conversations
customersdixa_customersid PK · linked to dixa_conversations
tagsdixa_tagsid PK · linked to dixa_conversations

FAQ

How does Datrise handle Dixa'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 Dixa 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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