Amazon S3 to Looker Studio Integration
AI-first ETL from Amazon S3 into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon S3 into Looker Studio
Datrise syncs Amazon S3's buckets, objects, prefixes, object metadata, versions, and storage classes into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.
Ideal for free, shareable dashboards on Google data sources.
Endpoints
Amazon S3: Object storage for files, exports, and data lakes.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Amazon S3 entities map to Looker Studio
| Amazon S3 entity | Looker Studio object | Notes |
|---|---|---|
| buckets | s3_buckets | id PK · custom fields → flattened columns for chart fields |
| objects | s3_objects | id PK · linked to s3_buckets |
| prefixes | s3_prefixes | id PK · linked to s3_buckets |
| object metadata | s3_object_metadata | id PK · linked to s3_buckets |
FAQ
How does Datrise handle Amazon S3's custom fields in Looker Studio?
Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.
How does the Amazon S3 to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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Early access
Connect Amazon S3 to Looker Studio the easy way
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