DatriseAI-first ETL

PostgreSQL to GoodData Integration

AI-first ETL from PostgreSQL into GoodData. Governed entities, incremental sync, typed landing tables.

How Datrise loads PostgreSQL into GoodData

Datrise syncs PostgreSQL's tables, rows, schemas, indexes, sequences, and replication slots 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

PostgreSQL: Self-managed PostgreSQL instances.

GoodData: Composable analytics platform with headless BI and embedded dashboards.

How PostgreSQL entities map to GoodData

PostgreSQL entityGoodData objectNotes
tableshp_postgres_tablesid PK · custom fields → flattened columns
rowshp_postgres_rowsid PK · linked to hp_postgres_tables
schemashp_postgres_schemasid PK · linked to hp_postgres_tables
indexeshp_postgres_indexesid PK · linked to hp_postgres_tables

FAQ

How does Datrise handle PostgreSQL'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 PostgreSQL to GoodData sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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