AI powers cloud platform operations and workload migrations

Unravel Data has introduced 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.

5 years ago Posted in

“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.”

Public cloud providers have gained considerable momentum, driven in part by increasing data applications adoption. Organisations have found the cloud to be an ideal place to run modern data apps due to big data’s elastic resource requirements. Additionally, the scarcity of data talent has driven organisations to adopt managed cloud services. However, cloud-based data pipelines still suffer from complexity challenges and lack of visibility into cost and resource usage at the application and user level. Organisations have limited insight into which applications and data pipelines are best suited for the cloud and how to size cloud instance requirements. Furthermore, these enterprises usually have no guidance on how to properly configure apps and resources once they’re in the cloud, where applications can behave very differently. In short, companies are migrating to the cloud with insufficient data to ensure performance service level agreements (SLAs) and cost targets are not placed at risk.

Unravel has addressed these challenges with its latest release, delivering unprecedented visibility, insights, recommendations and automation for optimising 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. Organisations 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:

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 minimises any risks associated with migrating to the cloud.
Cost reduction - Provides the most cost-effective instance recommendations based on detailed dependency understanding for minimising wasted capacity and overprovisioning.
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 centre. 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.

AI powers cloud platform operations and workload migrations

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