Acceldata takes on data reliability with low-code features

Based in California Speed ​​up datawhich provides an enterprise data observation platform, today expanded its offerings with new low-code/no-code capabilities to simplify how teams ensure data reliability.

Acceldata seeks to meet the needs of enterprises that are now doubling down on data-driven decision-making. Such companies invest in infrastructure (data lakes/warehouses) and add new resources every day, but the operational side remains quite complex and requires hard-to-get data engineering talent. This is where Acceldata claims to help, introducing low-code capabilities to address the issues of failing data pipelines, low-quality datasets, and rising data management costs.

The company provides a multi-dimensional observation platform that provides end-to-end insight into data processing power, data pipeline performance, and data quality across modern data stacks. It looks at all connected data and uses artificial intelligence (AI) and machine learning (ML) to help teams investigate and solve problems such as data system performance, under-resources and cost overruns.

What’s new on the platform?

With the latest update, Acceldata adds low-code/no-code options to its data reliability solution, automating and scaling to handle the most complex data quality and operational intelligence use cases. The move gives companies the option to either use low-code visual capabilities to create their own rules to ensure data reliability, or get up and running quickly with pre-built templates that require no coding at all. Users can write rules in four different implementation languages ​​to solve complex use cases, it added.

The update comes just a month after the company’s $50 million Series C round and marks another remarkable vendor-led effort to help global enterprises gain greater control over their data pipelines.

Unlike other observable solutions, the reliability engine, according to the company, goes beyond data quality and can also look at data freshness, data reconciliation, troubleshooting and drift.

“Data operations and architecture teams need a new approach to support modern analytics far beyond the current generation of data quality,” said Ashwin Rajeeva, co-founder and CTO of Acceldata, commenting on the new capabilities.

Intelligent alerts, self-healing for data reliability

“Having reliable data is critical as it moves and transforms in real time through the modern data stack. Acceldata is committed to continuously improving our data observation platform to provide the most comprehensive reliability solution that addresses the most complex data quality challenges efficiently and at scale,” he added.

In addition to low-code capabilities, the update also introduces intelligent alerts and targeted recommendations as new features.

Intelligent alerts, says Acceldata, will signal teams in advance to identify, isolate and remediate untrustworthy data. It provides visibility into basic and advanced compute, pipeline, and policy configurations – while prioritizing the most critical data asset alerts to aid in rapid recovery. Meanwhile, the targeted recommendations will provide ML-based guidance to address specific operational events such as long-running queries, dormant users, data table pattern changes, and unused data artifacts.

There’s also a new “self-healing” feature that encourages automatic remediation of certain issues, helping teams to respond to and resolve incidents faster. This will also reduce technical burdens and operational costs, the company said.

In the field of observability, Acceldata competes with Cribl, Monte Carlo and BigEye, among others. The presence of such tools has grown in recent years – a trend that is expected to continue with the constant increase in data volumes and systems. According to a survey conducted by Censuswide80% of enterprise data leaders have already expressed plans to prioritize investments in systems to provide visibility, and 85% plan to deploy data observation by 2023.