Bigeye

Bigeye

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

Teams

Bigeye

caters to:

Data
Data Engineering
Analytics Engineering
Analytics
Data Science
Machine Learning

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 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

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
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