Catch Data Issues Before They Catch You
ML-powered detection identifies unusual patterns, volume changes, and distribution shifts automatically. Know about problems before stakeholders ask why dashboards look wrong.
What We Detect
Sparvi monitors multiple dimensions of your data to catch issues early.
Volume Anomalies
Detect when row counts deviate from expected patterns. Catch partial loads, duplicate data, and failed pipelines.
Distribution Anomalies
Identify when values shift outside normal ranges. Catch calculation errors, unit changes, and data corruption.
Null Rate Spikes
Monitor null percentages across columns. Catch upstream changes, ETL bugs, and data source issues.
Freshness Issues
Track when data was last updated. Catch pipeline failures, source outages, and scheduling issues.
Pattern Breaks
Detect changes in expected patterns like seasonality and trends. Catch upstream business changes early.
Uniqueness Changes
Monitor cardinality and duplicate rates. Catch unintended duplicates and ID collision issues.
How Anomaly Detection Works
Learn Baselines
Sparvi analyzes your historical data to understand normal patterns—row counts, distributions, null rates, and more.
Monitor Continuously
As new data arrives, Sparvi compares it against established baselines using ML algorithms.
Alert on Deviations
When data falls outside expected ranges, Sparvi creates issues and alerts via Slack, email, or PagerDuty.
Why Teams Choose Sparvi for Anomaly Detection
Without Anomaly Detection
- ✗Stakeholders discover issues in dashboards
- ✗Hours spent investigating root causes
- ✗Decisions made on bad data
With Sparvi
- ✓Proactive alerts before stakeholders notice
- ✓AI suggestions speed up root cause analysis
- ✓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. This includes sudden changes in row counts, unusual null rate spikes, distribution shifts, and other unexpected patterns that may indicate data quality issues.
How does Sparvi detect data anomalies?
Sparvi uses machine learning to establish baselines from your historical data. It continuously monitors your data against these baselines, detecting when metrics deviate beyond configurable thresholds. Anomalies are automatically flagged and can trigger alerts via Slack, email, or PagerDuty.
Do I need to configure thresholds manually?
No. Sparvi's ML algorithms automatically learn what's normal for your data. You can optionally adjust sensitivity levels if you want more or fewer alerts, but no manual threshold configuration is required to get started.
How quickly will I be alerted to anomalies?
Sparvi can detect anomalies as soon as your scheduled profiling runs complete. For most teams, this means anomalies are detected within minutes to hours of occurring, rather than days later when stakeholders notice problems.
Stop Discovering Issues After the Fact
Get proactive anomaly detection that catches data issues before they impact your business.
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