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Acceldata Expands Data Observability Platform with New Data Reliability Capabilities

New features streamline data operations to solve complex data reliability challenges, improving performance.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Acceldata’s latest update to its data reliability solution offers significant enhancements, including no-code/low-code options, intelligent alerting, targeted recommendations, and self-healing capabilities to solve the most complex data reliability challenges while improving operational efficiency and reducing costs.

The supply chain for delivering trusted data has proven to be a top priority as modern organizations realize the business-critical value of data analytics. Data must have the highest degree of reliability to stay compliant and market-ready, yet data teams continue to face poor data freshness, completeness, and quality, limiting the ability for organizations to make informed, data-driven decisions.

Legacy data quality tools built for relational databases are unable to address data reliability issues at cloud scale. Several data observability tools focus on first-order metrics such as freshness and volume, which do not address the complex needs of enterprise data teams. 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 alerts: Alerts and advanced warning capabilities help enterprises prioritize and quickly identify, isolate, and remedy unreliable data. Alerts provide comprehensive insight into standard and advanced configurations across an organization's computing resources, pipelines, policies, and more.
  • Targeted recommendations: ML-based recommendations are provided for out-of-the-box 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 eliminate operational costs, reduce the engineering burden, and shorten the time to respond and resolve incidents.
  • No-code, low code, and complex rule authoring: Low-code visual capabilities allow 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.

“Data operations and architecture teams require a new approach to support data reliability needs for data and analytics that goes far beyond the current generation of data quality,” said Ashwin Rajeeva, co-founder and CTO of Acceldata. “Having reliable data is critical as it moves and transforms in real-time across the modern data stack.”

Click here to learn more about Acceldata’s data reliability solution.

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