DatriseAI-first ETL

Snowflake to MySQL Integration

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

How Datrise loads Snowflake into MySQL

Datrise syncs Snowflake's databases, schemas, tables, views, query history, and warehouses into MySQL as a typed table per source entity. Flexible or custom fields land in JSON columns, and timestamps such as created, updated, and status changes are typed as DATETIME/TIMESTAMP.

Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE, so re-runs update only what changed. Optional RANGE partitioning by load date. MySQL collation matters for CRM text, so Datrise lands utf8mb4 to preserve emoji and non-Latin characters.

Ideal for operational reporting and app databases already standardized on MySQL.

Endpoints

Snowflake: Cloud data platform with separated storage and compute.

MySQL: Widely used OSS relational engine (InnoDB).

How Snowflake entities map to MySQL

Snowflake entityMySQL objectNotes
databasessnowflake_databasesid PK · custom fields → JSON columns
schemassnowflake_schemasid PK · linked to snowflake_databases
tablessnowflake_tablesid PK · linked to snowflake_databases
viewssnowflake_viewsid PK · linked to snowflake_databases

FAQ

How does Datrise handle Snowflake's custom fields in MySQL?

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

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

It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE.

Related pipelines

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