Data observability · Bigeye

Bigeye

Data observability platform that auto-generates quality metrics and anomaly alerts across warehouses and lakes.

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Bigeye scans warehouse and lake tables to automatically generate baseline quality metrics such as row counts, null rates and distribution stats, then applies anomaly detection so teams get monitoring coverage without writing a check for every column by hand. It layers lineage-based root cause analysis on top, so an alert links back to the upstream table or job likely responsible, and supports custom SQL-based rules and unit-test-style checks for cases automated metrics miss. Data SLAs let teams set explicit freshness and quality targets for critical tables and report against them. Alerts route to Slack and PagerDuty, and a lightweight catalog view surfaces monitored assets. Bigeye is delivered as a cloud SaaS product and is generally chosen by data platform teams that want fast, automated coverage across a large warehouse footprint.

At a glance

Vendor Bigeye
Pricing model Quote only
Free tier No
Deployment Cloud
Open source No
Best for Data platform teams wanting automated observability coverage without hand-writing every check.

Pricing

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Features

  • Automated metric generation
  • ML-based anomaly detection
  • Data SLAs
  • Lineage-based root cause analysis
  • Custom SQL rules and unit tests
  • Slack and PagerDuty alerting
  • Lightweight catalog view

Integrations

Profile last reviewed September 21, 2026

Alternatives

Monte Carlo Data observability platform using ML-based anomaly detection to catch broken pipelines before they reach dashboards. Metaplane Lightweight data observability tool with a genuine free tier and per-table usage pricing, including Snowflake credit billing. Acceldata Enterprise data observability platform covering data reliability, pipeline performance and cloud data cost optimization.

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Terms to know

Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Diagnostic analytics Analytics focused on explaining why something happened by drilling into patterns, correlations, and root causes behind results. Root cause analysis (RCA) A structured process for tracing a problem back through its contributing factors to the underlying cause, not just its symptoms.

Related guides

How to choose a data observability tool Pick a data observability tool by how it generates coverage, where it needs to run relative to your data, and whether you're already living inside dbt.