Amazon RDS to Mode Integration
AI-first ETL from Amazon RDS into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon RDS into Mode
Datrise syncs Amazon RDS's tables, rows, schemas, indexes, primary keys, and change streams into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Amazon RDS: Managed relational database service on AWS.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Amazon RDS entities map to Mode
| Amazon RDS entity | Mode object | Notes |
|---|---|---|
| tables | amazon_rds_tables | id PK · custom fields → flattened columns for SQL and notebooks |
| rows | amazon_rds_rows | id PK · linked to amazon_rds_tables |
| schemas | amazon_rds_schemas | id PK · linked to amazon_rds_tables |
| indexes | amazon_rds_indexes | id PK · linked to amazon_rds_tables |
FAQ
How does Datrise handle Amazon RDS's custom fields in Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Amazon RDS to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Amazon RDS
Early access
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