Bigeye Introduces Metadata Metrics—Instant Data Observability for the Entire Data Warehouse

Data teams no longer need to choose between wide or deep coverage

Bigeye, the creators of the leading data observability platform, announced the release of Metadata Metrics which provides instant coverage for the entire data warehouse from the moment customers connect.

“We built Metadata Metrics so our customers can detect basic operational failures anywhere in their warehouses without lifting a finger”

Among data observability solutions, Bigeye is the only platform capable of broadly monitoring across tables and deeply into the most critical datasets, reducing the number of expensive outages affecting business-critical applications.

Instant data observability

Metadata Metrics scan existing query logs to automatically track key operational metrics, including the time since tables were last loaded, the number of rows inserted, and the number of read queries run on every dataset. Metadata Metrics take only minutes to set up, with zero manual configuration and almost no additional load to the warehouse.

Metadata Metrics provide customers with immediate insights into key operational attributes of every table including:

1. Time since the table was last refreshed
2. Number of rows inserted per day
3. Number of queries run per day

With Metadata Metrics enabled, data teams will be the first to know about stale data, table updates that are too big or too small, or changes in table utilization, thanks to Bigeye’s best-in-class anomaly detection system.

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T-Shaped Monitoring—wide and deep

Bigeye is the creator of T-shaped Monitoring, a unique approach to data observability that tracks fundamentals across all data while applying deeper monitoring on the most critical datasets, such as those used for financial planning, machine learning models, and executive-level dashboards. This approach ensures Bigeye customers are covered against the greatest number of “unknown unknown” data outages.

“We built Metadata Metrics so our customers can detect basic operational failures anywhere in their warehouses without lifting a finger,” said Kyle Kirwan, Bigeye CEO and co-founder. “Bigeye could already do deeper monitoring for our customers’ most critical tables better than any other platform. Now, we can also go really wide and monitor the basics on thousands of tables for them, instantly.”

Here’s how it works:

1. Enable Metadata Metrics to track the basics across all data in the warehouse instantly.
2. Go deep on each business-critical dataset using a blend of metrics that Bigeye suggests for each table from its library of 70+ pre-built data quality metrics.
3. Take it even further by adding custom metrics with Templates and Virtual Tables to ensure custom business logic is monitored for defects.

T-Shaped Monitoring gives data teams peace of mind with monitoring across the entire warehouse, 24/7. With Metadata Metrics, it’s even faster to set up and deploy broad coverage without the configuration hassle. As a result, Bigeye customers can detect both simple problems, such as stale data and even the most subtle errors in any critical dataset.