DatriseAI-first ETL

Paystack to ClickHouse Integration

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

How Datrise loads Paystack into ClickHouse

Datrise syncs Paystack's transactions, customers, plans, subscriptions, settlements, and refunds into ClickHouse as a MergeTree table per source entity. Flexible or custom fields land in JSON or Map columns, and timestamps such as created, updated, and status changes are typed as DateTime64.

Sync is incremental: Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge, so re-runs update only what changed. Partition by month and order by (entity id, updated-at) for fast range scans. ClickHouse deduplicates asynchronously on merge, so Datrise uses ReplacingMergeTree and FINAL-safe queries rather than assuming immediate upserts.

Ideal for high-volume event analytics that need sub-second aggregation.

Endpoints

Paystack: Online payment collection for African markets.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Paystack entities map to ClickHouse

Paystack entityClickHouse objectNotes
transactionspaystack_transactionsid PK · custom fields → JSON or Map 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 ClickHouse?

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

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

It runs incrementally — Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge.

Related pipelines

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