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New Unravel Data Release Introduces Cloud Platform Operations and Workload Migration Capabilities

Unravel Data announced a new portfolio of capabilities that help customers plan, migrate, and manage modern data applications running on AWS, Microsoft Azure and Google Cloud Platform.

This release leverages artificial intelligence, machine learning, and predictive analytics to baseline on-premises big data deployments and then determine which apps are the best candidates to move to the cloud based on customer defined criteria. Unravel can also help validate the success of a cloud migration and predict capacity based on the customers’ application workloads.

“All indications point to a massive shift in data deployments to the cloud,” said Kunal Agarwal, CEO, Unravel Data. “But there are too many unknowns around cost, visibility and migration that have prevented this transition to the cloud from occurring more quickly. We’re excited to introduce the industry’s only full-stack, AI-powered solution for migrating and managing data apps in the cloud.”

With its latest release, Unravel delivers visibility, insights, recommendations and automation for optimizing data workloads in the cloud. Unravel uses AI, machine learning and advanced analytics to determine the cloud infrastructure needs, the appropriate server instance sizes, and provide automated troubleshooting and auto-tuning of Spark, Hadoop, Kafka, and SQL/NoSQL powered data pipelines running on cloud platforms.

Unravel’s offering helps customers better migrate modern data pipelines to the cloud, establish and meet stringent SLAs for data apps in the cloud, and gain accounting and governance metrics for chargeback, capacity planning, and budget forecasting.

Unravel’s Cloud Operations capabilities include:

- Recommendations for the best apps to migrate – Unravel baselines on-premises performance of the full big data stack and uses AI to identify the best app candidates for migration to cloud. Organizations can avoid migrating apps that aren’t ideal for the cloud and having to repatriate them later.

- Full stack visibility – Unravel uses automation to provide detailed reports and metrics on app usage, performance, cost and chargebacks in the cloud.

- Unified management of the full big data stack on all deployment platforms – Unravel Cloud Migration covers AWS, Azure and Google clouds, as well as on-premises, hybrid environments and multi-cloud settings. Customers get AI-powered troubleshooting, auto-tuning and automated remediation of failures and slowdowns with the same user interface.

- Mapping on-premises infrastructure to cloud server instances – Unravel helps customers choose cloud instance types for their migration based on three strategies:
1. Lift and shift – A one-to-one mapping from physical servers to virtual servers, matching memory, storage and CPU/vCore footprints. This ensures that a cloud deployment will have the same (or more) amount of resources available as a current on-prem environment and minimizes any risks associated with migrating to the cloud.
2. Cost reduction - Provides the most cost-effective instance recommendations based on detailed dependency understanding for minimizing wasted capacity and overprovisioning.
3. Workload fit - Takes into account data collected over time from the on-premises environment, making recommendations for instance types based on the actual workload of applications running in a data center. These recommendations will be based on the VCore, memory, and storage requirements of a customer’s typical runtime environment.

- Cloud capacity planning and chargeback reporting - Unravel can predict cloud storage requirements up to six months out and can provide a detailed accounting of resource consumption and chargeback by user, department or other criteria.

- Migration validation - Unravel can provide a before and after assessment of cloud applications by comparing on-premises performance and resource consumption to the same metrics in the cloud, thereby validating the relative success of the migration.

Cloud services supported by the Unravel platform today include IaaS deployments on Azure, AWS and Google Cloud Platform and PaaS services on Azure HDInsight and AWS EMR. Supported services as part of Unravel’s early access program include AWS Redshift and AWS Athena.

Unravel is available now.

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New Unravel Data Release Introduces Cloud Platform Operations and Workload Migration Capabilities

Unravel Data announced a new portfolio of capabilities that help customers plan, migrate, and manage modern data applications running on AWS, Microsoft Azure and Google Cloud Platform.

This release leverages artificial intelligence, machine learning, and predictive analytics to baseline on-premises big data deployments and then determine which apps are the best candidates to move to the cloud based on customer defined criteria. Unravel can also help validate the success of a cloud migration and predict capacity based on the customers’ application workloads.

“All indications point to a massive shift in data deployments to the cloud,” said Kunal Agarwal, CEO, Unravel Data. “But there are too many unknowns around cost, visibility and migration that have prevented this transition to the cloud from occurring more quickly. We’re excited to introduce the industry’s only full-stack, AI-powered solution for migrating and managing data apps in the cloud.”

With its latest release, Unravel delivers visibility, insights, recommendations and automation for optimizing data workloads in the cloud. Unravel uses AI, machine learning and advanced analytics to determine the cloud infrastructure needs, the appropriate server instance sizes, and provide automated troubleshooting and auto-tuning of Spark, Hadoop, Kafka, and SQL/NoSQL powered data pipelines running on cloud platforms.

Unravel’s offering helps customers better migrate modern data pipelines to the cloud, establish and meet stringent SLAs for data apps in the cloud, and gain accounting and governance metrics for chargeback, capacity planning, and budget forecasting.

Unravel’s Cloud Operations capabilities include:

- Recommendations for the best apps to migrate – Unravel baselines on-premises performance of the full big data stack and uses AI to identify the best app candidates for migration to cloud. Organizations can avoid migrating apps that aren’t ideal for the cloud and having to repatriate them later.

- Full stack visibility – Unravel uses automation to provide detailed reports and metrics on app usage, performance, cost and chargebacks in the cloud.

- Unified management of the full big data stack on all deployment platforms – Unravel Cloud Migration covers AWS, Azure and Google clouds, as well as on-premises, hybrid environments and multi-cloud settings. Customers get AI-powered troubleshooting, auto-tuning and automated remediation of failures and slowdowns with the same user interface.

- Mapping on-premises infrastructure to cloud server instances – Unravel helps customers choose cloud instance types for their migration based on three strategies:
1. Lift and shift – A one-to-one mapping from physical servers to virtual servers, matching memory, storage and CPU/vCore footprints. This ensures that a cloud deployment will have the same (or more) amount of resources available as a current on-prem environment and minimizes any risks associated with migrating to the cloud.
2. Cost reduction - Provides the most cost-effective instance recommendations based on detailed dependency understanding for minimizing wasted capacity and overprovisioning.
3. Workload fit - Takes into account data collected over time from the on-premises environment, making recommendations for instance types based on the actual workload of applications running in a data center. These recommendations will be based on the VCore, memory, and storage requirements of a customer’s typical runtime environment.

- Cloud capacity planning and chargeback reporting - Unravel can predict cloud storage requirements up to six months out and can provide a detailed accounting of resource consumption and chargeback by user, department or other criteria.

- Migration validation - Unravel can provide a before and after assessment of cloud applications by comparing on-premises performance and resource consumption to the same metrics in the cloud, thereby validating the relative success of the migration.

Cloud services supported by the Unravel platform today include IaaS deployments on Azure, AWS and Google Cloud Platform and PaaS services on Azure HDInsight and AWS EMR. Supported services as part of Unravel’s early access program include AWS Redshift and AWS Athena.

Unravel is available now.

The Latest

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...