DatriseAI-first ETL

Source Dynamodb Google BigQuery

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

How Datrise loads Source Dynamodb into Google BigQuery

Datrise syncs Source Dynamodb'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

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Source Dynamodb entities map to Google BigQuery

Source Dynamodb entityGoogle BigQuery objectNotes
recordssource_dynamodb_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventssource_dynamodb_eventsTIMESTAMP events
configuration objectssource_dynamodb_configuration_objectsid PK · linked to source_dynamodb_records

FAQ

How does Datrise handle Source Dynamodb'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 Source Dynamodb 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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