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.
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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:
- Time since the table was last refreshed
- Number of rows inserted per day
- 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.”
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