Bigeye vs Cohesity
Side-by-side comparison of AI visibility scores, market position, and capabilities
Bigeye provides automated data monitoring with threshold-based and ML-driven anomaly detection for data warehouses to catch data quality issues at scale.
About
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.
Cohesity is a unicorn AI-powered data security and management platform consolidating backup, recovery, and data intelligence for enterprise hybrid environments.
About
Cohesity is an AI-powered data security and management platform that consolidates backup, disaster recovery, ransomware protection, data governance, and data intelligence for enterprise hybrid and multi-cloud environments on a scale-out distributed architecture designed to eliminate the legacy backup infrastructure complexity that has burdened enterprise IT for decades. The platform's hyperscale architecture pools backup storage and compute into a distributed cluster that scales horizontally by adding nodes, providing predictable linear performance scaling and eliminating the backup window limitations and media server bottlenecks of traditional backup architectures. Cohesity's data management layer provides a unified namespace over all backup data, enabling secondary use cases — test and development data provisioning, analytics, eDiscovery, and compliance search — that add business value to data that would otherwise sit inert in backup storage.
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