Day.ai → Redash
AI-first ETL from Day.ai into Redash. Governed entities, incremental sync, typed landing tables.
How Datrise loads Day.ai into Redash
Datrise syncs Day.ai's contacts, accounts, deals, activities, and lifecycle events into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.
Ideal for lightweight, query-driven dashboards.
Endpoints
Day.ai: AI-native CRM for relationship data, enrichment, and workflow automation.
Redash: Open-source SQL client for queries, visualizations, and dashboards.
How Day.ai entities map to Redash
| Day.ai entity | Redash object | Notes |
|---|---|---|
| contacts | day_ai_contacts | id PK · custom fields → flattened columns for query results |
| accounts | day_ai_accounts | id PK · linked to day_ai_contacts |
| deals | day_ai_deals | id PK · linked to day_ai_contacts |
| activities | day_ai_activities | temporal columns events |
FAQ
How does Datrise handle Day.ai's custom fields in Redash?
Flexible values are stored as flattened columns for query results, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Redash types.
How does the Day.ai to Redash sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
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