Unravel Data Launches Cloud Data Cost Observability and Optimization for Google Cloud BigQuery

New Functionality Delivers FinOps, AI-driven Cloud Cost Management and Performance Optimization for BigQuery Users

Unravel Data, the first AI-enabled data observability and FinOps platform built to address the speed and scale of modern data platforms, announced the release of Unravel 4.8.1, enabling Google Cloud BigQuery customers to see and better manage their cloud data costs by understanding specific cost drivers, allocation insights, and performance and cost optimization of SQL queries. This launch comes on the heels of the recent BigQuery pricing model change that replaced flat-rate and flex slot pricing with three new pricing tiers, and will help BigQuery customers to implement FinOps in real-time to select the right new pricing plan based on their usage, and maximize workloads for greater return on cloud data investments.

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“With Unravel’s built-in AI, BigQuery users have data observability and FinOps in one solution to increase data pipeline reliability and cost efficiency so that businesses can bring even more workloads to the cloud for the same spend.”

As today’s enterprises implement artificial intelligence (AI) and machine learning (ML) models, to continually garner more business value from their data, they are experiencing exploding cloud data costs, with a lack of visibility into cost-drivers and a lack of control for managing and optimizing their spend. As cloud costs continue to climb, managing cloud spend remains a top challenge for global business leaders. Data management services are the fastest-growing category of cloud service spending, representing 39% of the total cloud bill. Unravel 4.8.1 enables visibility into BigQuery compute and storage spend and provides cost optimization intelligence using its built-in AI to improve workload cost efficiency.

Unravel’s purpose-built AI for BigQuery delivers insights based on Unravel’s deep observability of the job, user, and code level to supply AI-driven cost optimization recommendations for slots and SQL queries, including slot provisioning, query duration, autoscaling efficiencies, and more. With Unravel, BigQuery users can speed cloud transformation initiatives by having real-time cost visibility, predictive spend forecasting, and performance insights for their workloads. BigQuery customers can also use Unravel to customize dashboards and alerts with easy-to-use widgets that offer insights on spend, performance, and unit economics.

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“As AI continues to drive exponential data usage, companies are facing more problems with broken pipelines and inefficient data processing which slows time to business value and adds to the exploding cloud data bills. Today, most organizations do not have the visibility into cloud data spend or ways to optimize data pipelines and workloads to lower spend and mitigate problems,” said Kunal Agarwal, CEO and Co-founder, Unravel Data. “With Unravel’s built-in AI, BigQuery users have data observability and FinOps in one solution to increase data pipeline reliability and cost efficiency so that businesses can bring even more workloads to the cloud for the same spend.”

“Enterprises are increasingly concerned about lack of visibility into and control of their cloud-related costs, especially for cloud-based analytics projects,” says Kevin Petrie, VP of Research at The Eckerson Group. “By implementing FinOps programs, they can predict, measure, monitor, optimize and account for cloud-related costs related to data and analytics projects.”

At the core of Unravel Data’s platform is its AI-powered Insights Engine, purpose-built for data platforms, which understands all the intricacies and complexities of each modern data platform and the supporting infrastructure to optimize efficiency and performance. The Insights Engine ingests and interprets the continuous millions of ongoing metadata streams to provide real-time insights into application and system performance, and recommendations to optimize costs and performance for operational and financial efficiencies.

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