Details of the round have surfaced through startup ecosystem reports and investor tracking platforms, which suggest the company intends to use the capital to scale its technology and expand internationally.

Validio develops automated monitoring systems that detect anomalies in large data pipelines, helping engineering teams ensure the reliability of datasets used in analytics and machine learning applications. Maintaining high-quality data has become increasingly critical as enterprises deploy AI systems that rely heavily on accurate and consistent information.

Earlier funding for the company included a $15 million seed round led by venture firm Lakestar, as announced by the investor.

Image: Validio

“Having reliable data is critical as it moves and transforms in real-time across the modern data stack. Acceldata is committed to continually enhancing our data observability platform to provide the most comprehensive reliability solution that addresses the most complex data quality challenges efficiently and at scale”,

said Ashwin Rajeeva, co-founder and CTO of Acceldata.

A reliable data supply chain is a top priority due to the business-critical value of data analytics and organizations’ awareness of this issue. Data must have the highest degree of reliability to remain compliant and market-ready, but data teams continue to face insufficient completeness and quality, limiting organizations’ ability to make informed, data-driven decisions.

Acceldata’s new, highly scalable data reliability engine automates and scales to handle the most complex data quality challenges across thousands of data pipelines in near real-time. Customers can now process hundreds of terabytes of data using the new engine that scales to any volume of data.

New capabilities include:

– Intelligent alerting: Alerting and advanced warning capabilities to prioritize and quickly identify, isolate, and remedy unreliable data. Alerts provide comprehensive insight into standard and advanced configurations across an organization’s compute, pipeline, policy, and more.
– Targeted recommendations: ML-based recommendations for out-of-the-box (OOB) data reliability use cases. Recommendations are included for specific occurrences such as pattern changes of data tables, unused data artifacts, and addition of checks to proactively resolve operational issues.
– Self-healing: Automated remediation features, which eliminate operational costs, reduces engineering burden, and accelerates the time to respond and resolve incidents.
– No-code, low code, and complex rule authoring: Low-code visual capabilities for data teams to build their own data reliability rules or get started quickly with pre-built templates. Users can author rules in four different implementation languages to solve for complex enterprise use cases.