Kyriba to Snowflake Integration
AI-first ETL from Kyriba into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Kyriba into Snowflake
Datrise syncs Kyriba's bank accounts, cash positions, payments, transactions, forecasts, and bank statements 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
Kyriba: Treasury, cash, and liquidity management.
Snowflake: Cloud data warehouse with separated compute and storage.
How Kyriba entities map to Snowflake
| Kyriba entity | Snowflake object | Notes |
|---|---|---|
| bank accounts | kyriba_bank_accounts | id PK · custom fields → VARIANT columns |
| cash positions | kyriba_cash_positions | id PK · linked to kyriba_bank_accounts |
| payments | kyriba_payments | id PK · linked to kyriba_bank_accounts |
| transactions | kyriba_transactions | id PK · linked to kyriba_bank_accounts |
FAQ
How does Datrise handle Kyriba'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 Kyriba 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.
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Early access
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