DatriseAI-first ETL

Paystack to CSV Files Integration

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

How Datrise loads Paystack into CSV Files

Datrise syncs Paystack's transactions, customers, plans, subscriptions, settlements, and refunds 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

Paystack: Online payment collection for African markets.

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

How Paystack entities map to CSV Files

Paystack entityCSV Files objectNotes
transactionspaystack_transactionsid PK · custom fields → JSON-encoded strings for nested fields
customerspaystack_customersid PK · linked to paystack_transactions
planspaystack_plansid PK · linked to paystack_transactions
subscriptionspaystack_subscriptionsid PK · linked to paystack_transactions

FAQ

How does Datrise handle Paystack'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 Paystack 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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