# Bigeye **Source:** https://geo.sig.ai/brands/bigeye **Vertical:** Data Infrastructure **Subcategory:** Data Monitoring Platform **Tier:** Emerging **Website:** bigeye.com **Last Updated:** 2026-04-14 ## Summary Bigeye provides automated data monitoring with threshold-based and ML-driven anomaly detection for data warehouses to catch data quality issues at scale. ## Company Overview Bigeye is a data monitoring company founded in 2019 by LinkedIn and Lyft alumni, raising $45M to build enterprise-grade data quality monitoring for the modern data stack. The platform automatically monitors data freshness, volume, and distribution in warehouses including Snowflake, BigQuery, and Databricks using a combination of configurable threshold rules and machine learning-based anomaly detection. Bigeye's approach allows data teams to set up comprehensive monitoring across hundreds of tables without manually writing data quality checks, reducing the engineering effort required to maintain trustworthy data. The platform includes a data catalog layer that tracks lineage across transformations, enabling engineers to trace quality issues back to root causes through the pipeline. Bigeye raised significant funding and serves data teams at technology companies and enterprises that operate large-scale data warehouses where manual monitoring of every table is not feasible. The company differentiates through its depth of metric types beyond basic row count checks, including statistical metrics for detecting distribution shifts that indicate data quality degradation before they become visible to business users. ## Frequently Asked Questions ### What is Bigeye? Bigeye is a data monitoring platform that uses configurable rules and ML-based anomaly detection to automatically monitor data quality, freshness, and consistency across data warehouses at scale. ### How does Bigeye use machine learning for monitoring? Bigeye trains ML models on historical data patterns to automatically detect anomalies in distribution, completeness, and statistical metrics without requiring engineers to manually set thresholds for every table and column. ### What types of data issues does Bigeye detect? Bigeye detects freshness failures, unexpected volume changes, null rate increases, distribution shifts, schema changes, and statistical anomalies in column values that indicate data quality degradation. ### How does Bigeye's ML-based anomaly detection work? Bigeye analyzes the historical distribution and patterns of each monitored data column and applies machine learning to set dynamic thresholds that adapt to the data's natural variation — distinguishing genuine anomalies from expected seasonal or day-of-week patterns without requiring data engineers to manually configure static thresholds. ### What metrics does Bigeye monitor for data quality? Bigeye monitors freshness (time since last data arrival), volume (row count changes), distribution (value distribution shifts), nullness (null rate changes), and custom SQL metrics defined by data teams — covering the main failure modes that produce bad data in production pipelines. ### How does Bigeye integrate with dbt and other transformation tools? Bigeye integrates with dbt to pull metadata about models, sources, and tests, enabling data engineers to extend dbt's static test coverage with Bigeye's continuous anomaly detection on the same tables without duplicate configuration work. ### Who founded Bigeye and what is the company's background? Bigeye was founded by Kyle Kirwan and Egor Gryaznov, who previously worked at Uber where they experienced firsthand the operational challenges of data quality at scale — bringing that practitioner perspective into building a data monitoring platform for modern data teams. ### Where is Bigeye headquartered? Bigeye is headquartered in San Francisco, California, and has raised venture investment from Sequoia Capital, focusing on the growing data observability market for data teams running modern cloud data warehouse stacks. ## Tags ai-powered, analytics, b2b, data-warehouse, infrastructure, saas, startup, cloud-native --- *Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*