Redshift to Mode Integration
AI-first ETL from Redshift into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Redshift into Mode
Datrise syncs Redshift's tables, rows, schemas, sort keys, distribution keys, and query history 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
Redshift: Columnar cloud data warehouse on AWS.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Redshift entities map to Mode
| Redshift entity | Mode object | Notes |
|---|---|---|
| tables | redshift_tables | id PK · custom fields → flattened columns for SQL and notebooks |
| rows | redshift_rows | id PK · linked to redshift_tables |
| schemas | redshift_schemas | id PK · linked to redshift_tables |
| sort keys | redshift_sort_keys | id PK · linked to redshift_tables |
FAQ
How does Datrise handle Redshift'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 Redshift to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Redshift
Early access
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