Redshift to GoodData Integration
AI-first ETL from Redshift into GoodData. Governed entities, incremental sync, typed landing tables.
How Datrise loads Redshift into GoodData
Datrise syncs Redshift's tables, rows, schemas, sort keys, distribution keys, and query history 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
Redshift: Columnar cloud data warehouse on AWS.
GoodData: Composable analytics platform with headless BI and embedded dashboards.
How Redshift entities map to GoodData
| Redshift entity | GoodData object | Notes |
|---|---|---|
| tables | redshift_tables | id PK · custom fields → flattened columns |
| rows | redshift_rows | id PK · linked to redshift_tables |
| schemas | redshift_schemas | id PK · linked to redshift_tables |
| sort keys | redshift_sort_keys | id PK · linked to redshift_tables |
FAQ
How does Datrise handle Redshift'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 Redshift to GoodData sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Redshift
Early access
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