Cito – Actionable data observability for data teams
Hi HN! We’re Clemens and Felix from Cito - thrilled to show you what we’ve built to help data engineers stay on top of data quality issues. Think Datadog meets Incident.io. Tests in dbt are great when checking whether specific expectations are true, but don’t work well for use cases where data patterns may change over time. When relying on testing alone, data teams regularly face situations where business stakeholders identify data issues in dashboards first, eroding trust. In such situations, understanding the implications of an issue and debugging can be a very manual and time-consuming…
In plain words
Cito is a data observability platform that helps data teams detect and respond to data quality issues. It monitors data pipelines using anomaly detection and custom tests to identify problems like row count changes and schema shifts, then provides context for debugging and impact analysis. Built for data engineers who need to catch issues before business stakeholders discover them in dashboards, Cito combines monitoring capabilities with incident management features to reduce manual investigation time.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN! We’re Clemens and Felix from Cito - thrilled to show you what we’ve built to help data engineers stay on top of data quality issues. Think Datadog meets Incident.io. Tests in dbt are great when checking whether specific expectations are true, but don’t work well for use cases where data patterns may change over time. When relying on testing alone, data teams regularly face situations where business stakeholders identify data issues in dashboards first, eroding trust. In such situations, understanding the implications of an issue and debugging can be a very manual and time-consuming process. To help data engineers ensure trust in data, Cito makes it easy to go beyond simple tests. By executing scheduled or near real-time out-of-the-box anomaly detection tests (row count, schema change, etc.) or custom SQL tests, data anomalies are detected and communicated in the context of the relevant column-level lineage via Slack. We believe data observability solutions should not stop at alerting teams to anomalies and our ambition is to support the complete end-to-end workflow of data engineers. Leveraging column-level lineage, our solution makes it straightforward to understand the context of anomalies. In addition, by automatically providing transparency in a git-blame-like fashion around ownership of data models and showing who made changes most recently, Cito helps to accelerate internal communications when troubleshooting. We’re super keen to hear your thoughts, ideas and experiences! You can also use our docs to try Cito in less than 15 min.
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