With ‘Deltas,’ Bigeye Sets Its Sights on Dataset Validation

As data sources and repositories grow larger and larger, companies are increasingly turning their eyes to data observability – being able to understand the health of all that data. Now, data observability firm Bigeye is launching Deltas, a tool that the company says quickly and automatically compares and validates multiple versions of any dataset.

Deltas (named for the use of ‘delta’ to signify change) applies the same observability configuration to pairs of datasets, identifying discrepancies between them regardless of the SQL dialect used by each. Bigeye says this process is done in seconds and works whether the copies exist in data warehouses, separate clouds, or pre-production staging grounds.

“We architected Bigeye to be an extensible framework, which allows us to apply data observability to all kinds of exciting use cases,” said Egor Gryaznov, CTO and co-founder at Bigeye. “We started by enabling data teams to automatically detect data quality and data pipeline issues. Now with Deltas, customers can easily compare and validate datasets. We look forward to enabling more groundbreaking user workflows through data observability in the near future.”

Deltas joins three other key features from Bigeye: Autometrics, which gauges data health metrics like recency, distribution, syntax, and more; Autothresholds, which automatically alerts users when thresholds are crossed in the data with minimal human intervention; and Integrations, through which Bigeye integrates data sources from providers like Snowflake, Amazon, and Google to allow better data observability.

The reveal of Deltas comes on the heels of Bigeye’s $45M Series B funding round, which was just announced in September. That round, in turn, came on the heels of a $17 million Series A just six months prior. When the Series B was announced, Bigeye CEO and c-founder Kyle Kirwan said the funding would be used to “scale the team, build our platform faster, and serve more customers than we normally would at such an early stage.”

Indeed, Bigeye has racked up a few big names in that time, including customers like Clubhouse, Instacart, and Udacity.

“Udacity has a strong data culture, and we have hundreds of datasets with new additions and enhancements released weekly. The ability to automatically compare datasets before promoting them to production allows our team to apply software engineering best practices, have greater confidence in our data, catch issues we would otherwise miss, and speed up our development process,” said Simon Dong, head of data engineering at Udacity.

Related Items

Bigeye Observes $45 Million in Funding

Bigeye Spawns Automated Data Quality Monitoring from Uber Roots