Bigeye
Data observability and monitoring platform
Bigeye is a data observability platform that automatically monitors data pipelines and warehouse tables for quality issues such as freshness delays, volume anomalies, and schema changes, alerting teams before bad data reaches downstream…
Definition
Bigeye is a data observability platform that automatically monitors data pipelines and warehouse tables for quality issues such as freshness delays, volume anomalies, and schema changes, alerting teams before bad data reaches downstream reports or applications. It uses automated anomaly detection rather than requiring every quality rule to be manually written in advance. Bigeye addresses the practical limit of rule-based data quality checks, which can only catch problems someone anticipated and wrote a rule for, by learning what normal data looks like and flagging unexpected deviations automatically.
Overview
Bigeye belongs to a category of tools known as data observability platforms, which apply a monitoring mindset, similar to application performance monitoring for software systems, to data itself. Rather than requiring data teams to anticipate and manually write every possible data quality rule in advance, Bigeye connects to a data warehouse and automatically profiles tables to learn typical patterns, such as expected row counts, null rates, or distinct value ranges, then flags statistically significant deviations from that learned baseline. A core capability is monitoring several standard dimensions of data health simultaneously: freshness, meaning whether data is arriving on schedule; volume, meaning whether row counts are within expected ranges; schema, meaning whether columns have been added, removed, or changed type unexpectedly; and distribution, meaning whether values in a column have shifted in ways that suggest an upstream problem. This multi-dimensional monitoring aims to catch a broader range of data incidents than rule-based checks focused narrowly on one type of issue. This automated, statistics-driven approach is what separates Bigeye from frameworks like Great Expectations, which rely primarily on explicitly authored rules; the tradeoff is that automated detection can surface unanticipated issues without upfront rule-writing, but may also require tuning to reduce false positive alerts as it learns what normal looks like for a given dataset. Bigeye integrates with common data warehouses such as Snowflake, BigQuery, and Redshift, and with orchestration tools like Airflow. In practice, teams use Bigeye to catch unexpected drops or spikes in table row counts, detect upstream schema changes before they break dashboards, and monitor data freshness for pipelines with strict service-level expectations. It also supports lineage tracking to help teams understand which downstream tables and dashboards are affected when an upstream data quality issue is detected, so alerts and incidents can be routed into existing workflows rather than requiring a separate incident process. Bigeye competes with other data observability platforms such as Monte Carlo and Databand, all addressing the broader problem of detecting data quality incidents automatically across increasingly complex modern data stacks, though teams needing precise, deterministic rules for known business logic often pair observability tools like Bigeye with rule-based frameworks rather than relying on anomaly detection alone. Smaller data stacks with only a handful of tables sometimes find manually written checks in a framework like Great Expectations sufficient without the added infrastructure of a dedicated observability platform, since automated profiling and anomaly detection deliver the most value once the number of tables and pipelines grows too large for any one person to monitor manually.
Key Features
- Automated data profiling to learn expected patterns without manual rules
- Monitoring across freshness, volume, schema, and distribution dimensions
- Anomaly detection flagging statistically significant deviations
- Integration with Snowflake, BigQuery, Redshift, and other warehouses
- Lineage tracking to identify downstream impact of data incidents
- Alert routing into existing tools like Slack and incident management systems
- Airflow integration for connecting alerts to orchestration workflows
- Dashboards summarizing overall data health across monitored tables