California-based Acceldata, which provides a data observability platform for enterprises, today expanded its offering with new low-code/no-code capabilities aimed at simplifying how teams ensure data reliability.
Acceldata seeks to meet the needs of enterprises that now double down on data-driven decision-making. Such companies are investing in infrastructure (data lakes/warehouses) and adding new sources every day, but the operational side of things remains quite complex, requiring hard-to-get data engineering talent. This is where Acceldata claims to help, introducing low-code capabilities to meet the problems of failing data pipelines, low-quality datasets and rising data management costs.
The company offers a multidimensional observability platform that provides end-to-end visibility into data processing power, data pipeline performance and data quality across modern data stacks. It looks at all connected data and employs artificial intelligence (AI) and machine learning (ML) to help teams investigate and remediate such issues as data system performance, lack of resources and cost overruns.
What’s new in the platform?
With the latest update, Acceldata is adding low-code/no-code options to its data reliability solution, which automates and scales to handle the most complex data quality and operational intelligence use cases. The move will give enterprises the option to either use low-code visual capabilities to build their own rules for ensuring data reliability or get started quickly with pre-built templates requiring no coding at all. Users can author rules in four different implementation languages to solve for complex enterprise use cases, it added.
The update comes just a month after the company’s series C round of $50 million and marks another notable vendor-driven effort to help global enterprises get a better grip on their data pipelines.
Unlike other observability solutions, according to the company, its reliability engine goes beyond data quality and can also look at data freshness, data reconciliation, problem resolution, and drift.
“Data operations and architecture teams require a new approach to support modern analytics that goes far beyond the current generation of data quality,” Ashwin Rajeeva, co-founder and CTO of Acceldata, said while commenting on the new capabilities.
Intelligent alerting, self-healing for data reliability
“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,” he added.
Beyond low-code capabilities, the update also introduces intelligent alerting and targeted recommendations as new features.
Intelligent alerting, Acceldata says, will signal teams in advance so as to identify, isolate, and remedy unreliable data. It will provide insights into standard and advanced configurations across compute, pipeline and policy – this while prioritizing the most critical alerts across data assets to help with quick remediation. Meanwhile, the targeted recommendations will issue ML-based guidance to address specific operational occurrences such as long-running queries, dormant users, pattern changes of data tables and unused data artifacts.
There’s also a new ‘self-healing’ feature that will drive automated remediation of certain issues, enabling teams to respond to and resolve incidents more quickly. This will also reduce engineering burden and operational costs, the company said.
In the observability space, Acceldata competes with the likes of Cribl, Monte Carlo and BigEye. The presence of such tools has grown in the last few years – a trend that is expected to continue with the constant increase in data volumes and systems. According to a survey conducted by Censuswide, 80% of enterprise data leaders have already expressed plans to prioritize investments in systems to provide visibility, and 85% plan to deploy data observability in 2023.
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