DatriseAI-first ETL

Teradata to Google BigQuery Integration

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

How Datrise loads Teradata into Google BigQuery

Datrise syncs Teradata's tables, rows, schemas, indexes, primary indexes, and query history 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

Teradata: Teradata analytical database for enterprise workloads.

Google BigQuery: Serverless analytics warehouse on GCP.

How Teradata entities map to Google BigQuery

Teradata entityGoogle BigQuery objectNotes
tablesteradata_d_tablesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
rowsteradata_d_rowsid PK · linked to teradata_d_tables
schemasteradata_d_schemasid PK · linked to teradata_d_tables
indexesteradata_d_indexesid PK · linked to teradata_d_tables

FAQ

How does Datrise handle Teradata'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 Teradata 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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