CSV File to Google BigQuery Integration

How Datrise loads CSV File into Google BigQuery

Datrise syncs CSV File's rows, columns, headers, delimiters, file batches, and encodings 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

CSV File: Delimited flat files from exports and drops.

Google BigQuery: Serverless analytics warehouse on GCP.

How CSV File entities map to Google BigQuery

CSV File entity	Google BigQuery object	Notes
rows	`csv_file_rows`	id PK · custom fields → JSON or nested/repeated (STRUCT) columns
columns	`csv_file_columns`	id PK · linked to csv_file_rows
headers	`csv_file_headers`	id PK · linked to csv_file_rows
delimiters	`csv_file_delimiters`	id PK · linked to csv_file_rows

FAQ

How does Datrise handle CSV File'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 CSV File 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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