Catch Data Issues Before They Catch You
Every Sparvi monitor can run in ML / statistical mode, learning a baseline from your data and flagging deviations automatically. Z-score, IQR, and moving-average detection across row counts, columns, and custom SQL, segmented by region, product, tenant, or any dimension you care about.
What We Detect
Sparvi monitors multiple dimensions of your data to catch issues early.
Volume Anomalies
Row-count monitors detect when volumes deviate from expected patterns. Catch partial loads, duplicate data, and failed pipelines.
Distribution Anomalies
Column metric monitors (avg, stddev, min, max) flag when values shift outside normal ranges. Catch calculation errors, unit changes, and data corruption.
Null Rate Spikes
Null % monitors across columns catch upstream changes, ETL bugs, and data source issues without writing any SQL.
Freshness Issues
Use a custom SQL monitor on max(updated_at) or a watermark column. Catch pipeline failures, source outages, and scheduling issues.
Segment Anomalies
Any monitor can be segmented by region, product, tenant, or any dimension. Each segment has its own baseline, so a problem in one slice never hides in the average.
Uniqueness Changes
Distinct count and distinct % monitors catch unintended duplicates, ID collisions, and cardinality drift.
How Anomaly Detection Works
Learn Baselines
Each monitor in statistical mode analyzes the configured baseline window of historical data, typically 14 days, to understand normal patterns for row counts, distributions, null rates, and more.
Monitor Continuously
On its configured schedule (every N minutes, daily, or weekly), the monitor compares the latest value against the baseline using z-score, IQR, or moving-average detection.
Alert on Deviations
When a value falls outside the expected range, and minimum data points has been met, Sparvi creates an issue with the segment name (if applicable) and routes alerts via Slack, email, or PagerDuty.
Why Teams Choose Sparvi for Anomaly Detection
Without Sparvi
- ✗Stakeholders discover issues in dashboards
- ✗Aggregates mask outages in individual segments
- ✗Decisions made on bad data
With Sparvi
- ✓Proactive alerts before stakeholders notice
- ✓Per-segment baselines surface localized issues immediately
- ✓Confidence in data-driven decisions
Frequently Asked Questions
What is anomaly detection in data quality?
Anomaly detection in data quality identifies data points, patterns, or values that deviate significantly from expected behavior. In Sparvi this is how a monitor in statistical evaluation mode raises an issue, when the latest value of the monitored metric falls outside the baseline learned from history.
How does Sparvi detect data anomalies?
Each Sparvi monitor in ML / statistical mode learns a baseline from your historical data using z-score, IQR, or moving-average methods. When the monitored value deviates beyond your configured sensitivity, Sparvi creates an issue and routes alerts to Slack, email, or PagerDuty. For segmented monitors, each segment has its own baseline.
Do I need to configure thresholds manually?
Not for statistical monitors, the baseline is learned automatically. If you have a hard contract (like "revenue should never drop below $50K/day"), switch that monitor to threshold mode and set warning and critical values explicitly. The two modes can coexist across your monitor library.
How quickly will I be alerted to anomalies?
Each monitor has its own schedule, every N minutes, daily, or weekly. The minimum interval is 5 minutes, and most teams find that 60-minute checks on critical tables strike the right balance between latency and warehouse cost.
Stop Discovering Issues After the Fact
Get proactive anomaly detection that catches data issues before they impact your business.
Start Free Trial