Google BigQuery to Amazon S3 Data Lake Integration
AI-first ETL from Google BigQuery into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google BigQuery into Amazon S3 Data Lake
Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.
Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.
Endpoints
Google BigQuery: Serverless analytics warehouse on Google Cloud.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Google BigQuery entities map to Amazon S3 Data Lake
| Google BigQuery entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| datasets | bigquery_datasets | id PK · custom fields → nested struct/map fields in Parquet |
| tables | bigquery_tables | id PK · linked to bigquery_datasets |
| views | bigquery_views | id PK · linked to bigquery_datasets |
| query jobs | bigquery_query_jobs | id PK · linked to bigquery_datasets |
FAQ
How does Datrise handle Google BigQuery's custom fields in Amazon S3 Data Lake?
Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.
How does the Google BigQuery to Amazon S3 Data Lake sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.
Related pipelines
More destinations for Google BigQuery
- Google BigQuery → Azure Data Lake Storage
- Google BigQuery → Azure Synapse
- Google BigQuery → Spreadsheets
- Google BigQuery → Airtable
- Google BigQuery → CSV Files
- Google BigQuery → MongoDB
- Google BigQuery → Supabase
- Google BigQuery → Neon
- Google BigQuery → PlanetScale
- Google BigQuery → Amazon DynamoDB
- Google BigQuery → Looker
- Google BigQuery → Looker Studio
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Early access
Connect Google BigQuery to Amazon S3 Data Lake the easy way
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