Acceldata Improves its Data Reliability Solution to Comprehensively Support the Modern Enterprise

Acceldata, the data observability company, is releasing new improvements to its data reliability solution, which is equipped with a highly scalable, expedited data reliability engine for handling complex data quality and addresses operational intelligence use cases. The enhancements focus on delivering low-code/no-code options, intelligent alerting, targeted recommendations, and self-healing capabilities for increasing operational efficiency at a lower cost.

With a variety of data reliability solutions on the market, legacy solutions fail to address issues of data freshness, completeness, and quality at scale, while modern solutions only focus on freshness and volume, ultimately ignoring the entirety of modern data teams’ challenges, according to the vendor.

“When we started working with enterprise data teams, we realized that they used legacy tools that were cumbersome to use, couldn’t create data quality rules, and did not scale and execute the tasks within time,” said Ashwin Rajeeva, co-founder and CTO of Acceldata. “Data teams are continually facing poor data freshness, completeness, and quality, limiting the ability for organizations to make informed, data-driven decisions.”

Acceldata aims to provide the most comprehensible data observability platform, which can be applied to a broad quantity of use cases to empower organizations to make informed, data-driven decisions. The platform is further supported by an advanced engine that solves complex data quality challenges across thousands of data pipelines, quickly.

“Modern data reliability provides data teams with the complete visibility into their data assets, pipelines, and processes necessary to make data products successful,” said Rajeeva. “Data reliability is a major step forward from traditional data quality. It includes data quality but covers much more functionality that data teams need to support for modern, near real-time data processes.”

Native no-code, low code options accompanying Acceldata’s enhancements to its platform allows data teams to construct their own data reliability rules or get started quickly with prefabricated templates. Users can also customize user defined functions (UDF) to generate specific rules that fit their unique needs.

With intelligent alerting, users will now receive holistic insights into both standard and advanced configurations throughout an enterprise’s compute, pipeline, and policy. Critical alerts are prioritized so incidents can be quickly remediated and more deeply examined with root cause analysis.

Acceldata’s data reliability solution is further outfitted with targeted recommendations, designed to resolve operational issues more proactively for specific occurrences—such as dormant users, running queries, and unused tables.

The solution’s new self-healing capabilities applies automated remediation functions to reduce operational costs and engineering burden, while simultaneously expediting response and resolving time for incidents. The platform also includes a cost explorer and trend dashboards, enabling users to track and analyze spend and utilization.

“The Acceldata data observability platform comprehensively correlates events across data, processing, and pipelines to transform how organizations build and operate data products,” concluded Rajeeva. “Data engineering, data quality, data reliability, and platform teams can achieve success in their data journey across cloud-native, multi-cloud, hybrid cloud, or on-premises data systems.”

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