JSON File to Birst Integration
AI-first ETL from JSON File into Birst. Governed entities, incremental sync, typed landing tables.
How Datrise loads JSON File into Birst
Datrise syncs JSON File's documents, nested objects, arrays, keys, file batches, and schemas into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.
Ideal for networked, governed enterprise BI.
Endpoints
JSON File: Structured JSON documents from exports and drops.
Birst: Cloud BI with networked analytics and enterprise semantic layers.
How JSON File entities map to Birst
| JSON File entity | Birst object | Notes |
|---|---|---|
| documents | json_file_documents | id PK · custom fields → flattened columns |
| nested objects | json_file_nested_objects | id PK · linked to json_file_documents |
| arrays | json_file_arrays | id PK · linked to json_file_documents |
| keys | json_file_keys | id PK · linked to json_file_documents |
FAQ
How does Datrise handle JSON File's custom fields in Birst?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Birst types.
How does the JSON File to Birst sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.
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Early access
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