Unravel Data will introduce new capabilities for operationalizing big data applications in production at scale with the launch of Unravel 4.4.
Unravel 4.4 introduces new innovations and capabilities to simplify operations whilst ensuring big data applications are highly reliable and predictable under the most demanding of eEnterprise big data and analytical workloads.
“We were the first in the category to deliver automated configuration tuning recommendations. Our latest release builds on that, leveraging our Machine Learning engine to extend automated tuning recommendations to an entire cluster for all tenants,” said Kunal Agarwal, CEO, Unravel Data. “Users get an end-to-end, granular view that shows how each application is performing across all infrastructure dependencies. Combined with other new capabilities in this release, we are helping eliminate the painstaking, trial-and-error configuration, troubleshooting and remediation workflow that’s long plagued big data Operations teams and now made it possible to manage large scale deployments in an agile and dependable manner.”
In order to accommodate growing enterprise big data deployments, Unravel 4.4 incorporates several major innovations. These include:
- Automation. Unravel 4.4 features new auto-tuning capabilities and sessions frameworks, enabling users to tune jobs automatically to a desired goal (such as speedup, SLA, and resource efficiency). Unravel leverages artificial intelligence operations (AIOps) optimization techniques to converge operation metrics with recommendations and actions to deliver a desired business outcome.
- Capacity Planning: Big data clusters tend to accumulate lots of data and operators are constantly under pressure to add more capacity to meet demands. Traditional approaches to capacity planning are very labor-intensive and exacerbate the problem through the use of ad-hoc spreadsheet models and reliance on tribal knowledge of cluster administrators. With the addition of capacity planning reporting, Unravel 4.4 provides an intuitive and data-driven approach to visualize and predict the growth of the cluster and make an informed decisions around capacity and resource requirements.
- Cluster Optimization: Unravel has long provided actionable insights and recommendations on a per-application basis. The platform was the first in the industry to provide recommendations for tuning precise configuration parameters. Unravel 4.4 extends this capability to provide configuration tuning recommendation at the entire cluster level. With these global recommendations, cluster administrators can now improve the performance of all jobs and applications running on the cluster.
- NoSQL Support: This release adds support for NoSQL and HBase. Unravel 4.4 provides an APM-centric view for HBase usage and helps identify anomalies and outlier issues (for example, table/region hotspotting) that could adversely affect the application, while providing remediation techniques.
- Small Files Analysis: This release offers a new reporting feature that gives users a comprehensive view of entire directories and a granular look at small files. This feature allows users to reduce resource utilization by these small files, freeing resources for larger workloads.
Unravel 4.4 will be generally available in September 2018.
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 ...