CSV File to Yellowfin Integration
AI-first ETL from CSV File into Yellowfin. Governed entities, incremental sync, typed landing tables.
How Datrise loads CSV File into Yellowfin
Datrise syncs CSV File's rows, columns, headers, delimiters, file batches, and encodings into Yellowfin as warehouse tables Yellowfin builds views on. 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 connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.
Ideal for dashboards with automated data storytelling.
Endpoints
CSV File: Delimited flat files from exports and drops.
Yellowfin: BI suite with dashboards, automated insights, and data storytelling.
How CSV File entities map to Yellowfin
| CSV File entity | Yellowfin object | Notes |
|---|---|---|
| rows | csv_file_rows | id PK · custom fields → flattened 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 Yellowfin?
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 Yellowfin types.
How does the CSV File to Yellowfin sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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