DatriseAI-first ETL

PostgreSQL to CSV Files Integration

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

How Datrise loads PostgreSQL into CSV Files

Datrise syncs PostgreSQL's tables, rows, schemas, indexes, sequences, and replication slots into CSV Files as one CSV per source entity. Flexible or custom fields land in JSON-encoded strings for nested fields, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes a fresh, fully-typed CSV per entity each run, so re-runs update only what changed. Optional date-suffixed files for change tracking. CSV has no types, so Datrise emits a companion schema and quotes/escapes consistently so downstream loaders don't misparse commas and newlines.

Ideal for portable hand-off into any tool that ingests delimited files.

Endpoints

PostgreSQL: Self-managed PostgreSQL instances.

CSV Files: Flat-file destination for exports and lightweight data sharing.

How PostgreSQL entities map to CSV Files

PostgreSQL entityCSV Files objectNotes
tableshp_postgres_tablesid PK · custom fields → JSON-encoded strings for nested fields
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 CSV Files?

Flexible values are stored as JSON-encoded strings for nested fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native CSV Files types.

How does the PostgreSQL to CSV Files sync stay up to date?

It runs incrementally — Datrise uses writes a fresh, fully-typed CSV per entity each run.

Related pipelines

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