As a data monitoring and alerting tool, Bigeye automatically detects data quality anomalies before data reaches end-users and speeds up resolution.

MonitoringData Governance

What need does Bigeye fulfill?

Users get frustrated when their data products break or data assets are not accurate. More often than not, breakages happen due to data quality issues such as delays, duplication, missing or malformed data, or data containing outliers. And these issues can affect a model, a dashboard, or even an entire application.

Data engineering teams, on the other hand, manage rapidly evolving data from a variety of internal and external sources, with data being transformed at multiple steps, often at the scale of hundreds or even thousands of tables. Bigeye is designed to make it easy for data teams to identify and resolve data quality problems proactively before something breaks and before end-users are affected.

What are the benefits of using Bigeye?

  Fewer user-reported data quality issues
  Faster time to problem detection
  Faster time to root cause issues and resolution
  Better tracking of data quality levels and trends
  Increased data user NPS and/or levels of trust

What are the core features of Bigeye?

  50+ pre-built data quality metrics like freshness, duplicates, etc
  Autometrics — suggests metrics automatically based on data profiling
  Autothresholds — sets and adjusts alert thresholds automatically
  Root-cause analysis queries that automatically identify problematic rows
  Customizable metrics definitions
  Alert channels like Slack, email, and webhooks
  Airflow operator for configuring monitoring from within Airflow jobs
  Support for popular data sources like Snowflake, BigQuery, and Presto
  Performance optimizations to reduce the monitoring tax on the warehouse

Which teams does Bigeye cater to?

Machine Learning
Analytics Engineering
Data Engineering
Data Science

Authored By

Kyle Kirwan's profile on Data-led Academy

Kyle Kirwan

Co-founder, CEO