DatriseAI-first ETL

Sentry to Redash Integration

AI-first ETL from Sentry into Redash. Governed entities, incremental sync, typed landing tables.

How Datrise loads Sentry into Redash

Datrise syncs Sentry's issues, events, releases, projects, stack traces, and environments 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

Sentry: Application error tracking and release health.

Redash: Open-source SQL client for queries, visualizations, and dashboards.

How Sentry entities map to Redash

Sentry entityRedash objectNotes
issuessentry_issuesid PK · custom fields → flattened columns for query results
eventssentry_eventstemporal columns events
releasessentry_releasesid PK · linked to sentry_issues
projectssentry_projectsid PK · linked to sentry_issues

FAQ

How does Datrise handle Sentry'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 Sentry to Redash sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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