Monitoring data pipelines means watching not just whether jobs run but whether the data they produce is fresh, complete and correct, so a silent data failure is caught before it reaches dashboards and decisions.
A data pipeline can succeed technically while producing wrong data, and that silent failure is worse than a crash because nobody notices until a report is off. DataOps monitoring watches four dimensions, freshness, volume, schema and lineage, so a broken pipeline is caught by its output, not just its exit code.
A job that completes with exit code zero tells you it ran, not that the data is right. It can process a truncated source, drop rows, or write stale data and still 'succeed'. Effective monitoring watches the data itself: is it fresh, is the row count in the expected range, does the schema still match, did upstream sources arrive.
Freshness (is the data up to date), volume (is the row or record count in the normal range), schema (has the structure changed unexpectedly), and lineage (did all upstream inputs arrive and are they healthy). Together these catch the silent failures that a job-status check alone misses, and tie a downstream data problem to the pipeline stage that caused it.
Ops Singularity's DataOps pillar treats freshness, volume, schema and lineage as first-class signals, and Sentinel AI acts on data incidents, a failed batch, a stalled stream, a freshness breach, through governed Action Tickets, so a silent data failure becomes an incident that gets resolved.
Job monitoring checks whether a pipeline ran; data pipeline monitoring checks whether the data it produced is fresh, complete and correct. A job can succeed while the data is wrong.
Freshness, volume, schema and lineage, so you catch silent data failures and can tie a downstream problem to the stage that caused it.
Bring a real incident. We will show you Sentinel investigate, act and verify end to end, with every action reversible and audited.