Amazon S3 to Holistics Integration
AI-first ETL from Amazon S3 into Holistics. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon S3 into Holistics
Datrise syncs Amazon S3's buckets, objects, prefixes, object metadata, versions, and storage classes into Holistics as warehouse tables modeled in Holistics. Flexible or custom fields land in flattened columns for the modeling layer, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the modeled tables, so re-runs update only what changed. Date-partitioned facts for fast aggregates. Holistics models data as code on top of SQL, so Datrise lands stable column names to keep your models from drifting.
Ideal for as-code BI modeling on a warehouse.
Endpoints
Amazon S3: Object storage for files, exports, and data lakes.
Holistics: Self-service BI with modeling layers and scheduled report delivery.
How Amazon S3 entities map to Holistics
| Amazon S3 entity | Holistics object | Notes |
|---|---|---|
| buckets | s3_buckets | id PK · custom fields → flattened columns for the modeling layer |
| 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 Holistics?
Flexible values are stored as flattened columns for the modeling layer, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Holistics types.
How does the Amazon S3 to Holistics sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the modeled tables.
Related pipelines
More destinations for Amazon S3
Early access
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