Real-Time Data Observability
Autonomous monitoring for your data pipelines and tables: freshness, volume, schema, and distribution checks that catch issues before they reach a report or AI model.
What this service covers
- data observability
- real-time data monitoring
- data pipeline monitoring
- data quality monitoring
- observability agents
- anomaly detection data
- data freshness alerting
Frequently asked questions
What is data observability?
Data observability is continuous, automated monitoring of data pipelines and tables. It watches freshness, volume, schema, and value distributions, and alerts the owner the moment something drifts, so issues are caught before they reach a dashboard, a decision, or an AI model.
How is this different from infrastructure monitoring?
Infrastructure monitoring tells you the server is up. Data observability tells you the data is correct. A pipeline can run successfully and still load wrong or stale data; observability catches that, uptime monitoring does not.
Which tools do you use?
We are tool-neutral. We implement Monte Carlo, Anomalo, Bigeye, or open-source checks depending on your stack, budget, and how much coverage you need. The tool matters less than tuning it and assigning ownership.
Why do we need this if we have a data team firefighting issues?
Firefighting means you find issues after they cause damage. Observability moves detection to the moment of breakage, so the same team resolves a flagged anomaly instead of explaining a wrong board number after the fact.
Will it just add more alert noise?
Only if it is untuned. We set thresholds against your real history and route each alert to a named owner, so an alert firing means something is actually wrong and someone is accountable for it.
How long does it take to stand up?
Monitoring the critical tables and pipelines is usually a 6 to 10 week engagement, including threshold tuning and the ownership model. Coverage expands from there.
Request the 30-day Analytics Truth Audit to scope this engagement for your environment.