DatriseAI-first ETL

Paystack to Snowflake Integration

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

How Datrise loads Paystack into Snowflake

Datrise syncs Paystack's transactions, customers, plans, subscriptions, settlements, and refunds into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

Paystack: Online payment collection for African markets.

Snowflake: Cloud data warehouse with separated compute and storage.

How Paystack entities map to Snowflake

Paystack entitySnowflake objectNotes
transactionspaystack_transactionsid PK · custom fields → VARIANT columns
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 Snowflake?

Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.

How does the Paystack to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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