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Unravel 4.8.1 Released

Unravel Data 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.

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.

“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.”

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.

Unravel 4.8.1 includes additional features, such as:

- Recommendations for baseline and max setting for reservations

- Scheduling insights for recurring jobs

- SQL insights and anti-patterns

- Recommendations for custom quotas for projects and users

- Top-K projects, users, and jobs

- Showback by compute and storage types, services, pricing plans, etc.

- Chargeback by projects and users

- Out-of-the-box and custom alerts and dashboards

- Project/Job views of insights and details

- Side-by-side job comparisons

- Data KPIs, metrics, and insights such as size and number of tables and partitions, access by jobs, hot/warm/cold tables

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Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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Unravel 4.8.1 Released

Unravel Data 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.

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.

“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.”

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.

Unravel 4.8.1 includes additional features, such as:

- Recommendations for baseline and max setting for reservations

- Scheduling insights for recurring jobs

- SQL insights and anti-patterns

- Recommendations for custom quotas for projects and users

- Top-K projects, users, and jobs

- Showback by compute and storage types, services, pricing plans, etc.

- Chargeback by projects and users

- Out-of-the-box and custom alerts and dashboards

- Project/Job views of insights and details

- Side-by-side job comparisons

- Data KPIs, metrics, and insights such as size and number of tables and partitions, access by jobs, hot/warm/cold tables

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...