CSV File to GoodData Integration
AI-first ETL from CSV File into GoodData. Governed entities, incremental sync, typed landing tables.
How Datrise loads CSV File into GoodData
Datrise syncs CSV File's rows, columns, headers, delimiters, file batches, and encodings into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.
Ideal for embedded, multi-tenant analytics.
Endpoints
CSV File: Delimited flat files from exports and drops.
GoodData: Composable analytics platform with headless BI and embedded dashboards.
How CSV File entities map to GoodData
| CSV File entity | GoodData 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 GoodData?
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 GoodData types.
How does the CSV File to GoodData sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for CSV File
Early access
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