Nasuni Corporation, a provider of file data services, has acquired the technology and products of DBM Cloud Systems.
Founded in 2016 by Dixon Doll, Jr., DBM delivers cloud-native data migration technology for hybrid and multi-cloud environments. The acquisition comes on the heels of a $60 million growth equity investment from Sixth Street in March. Dr. Joseph Slember, formerly DBM’s vice president of engineering, has joined Nasuni as vice president of engineering, leading the data mobility team.
Enterprises are aggressively adopting cloud-first approaches to data management in order to strengthen cyber resilience, support hybrid work models and get frictionless scale to meet business needs. Data migration is an important part of any cloud-first strategy. Moving data from legacy file storage systems to the cloud and between clouds has to be frictionless. Since 2021, nearly all companies (93%) have adopted a multi-cloud strategy, according to Accenture, making high-speed data mobility between clouds a critical functionality.
With this acquisition, Nasuni will enhance its data mobility capabilities to provide seamless data migration and enhanced multi-cloud support for its customers so they can effortlessly move data from on-premises to the cloud and between different cloud hyperscalers. The integrated technology will enable Nasuni to enhance its capabilities around data mobility, including:
• Cloud data migration: Migrate petabytes of data from on-premises to the cloud or across public, private or hybrid clouds.
• Intelligent tiering: Automatically shift data across cloud storage tiers to optimize costs.
• Enhanced multi-cloud support: Move and maintain data in close proximity to desired cloud services and applications for optimal performance while minimising security risk through flexible data portability.
Slember said: “At DBM, we built technology that automates data migration and management between clouds while simplifying the movement of data among them.
“By combining the technology with Nasuni’s file data services platform, users will gain faster file data migrations and more intelligent tiering, saving significant time and costs.”
Paul Flanagan, chief executive officer at Nasuni, said: “The Nasuni File Data Platform includes the world’s only cloud-native global file system that combines file and object storage delivering unlimited scale and capacity and we’ve built strong partnerships with Amazon AWS, Microsoft Azure and Google Cloud for the backend object storage.
“We are making file data migration, tiering and data movement between clouds a frictionless service. Today, enterprises do not want to lock in all their data with a single cloud, but are instead adopting a multi-cloud approach. DBM’s technology and the expertise we’ve gained will enable Nasuni to accelerate and innovate our support for data migration, multi-cloud and data mobility.”
How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning
In the event of a severe incident for your business, you need to analyze what exactly changed (the root cause) to understand its impact.
Using machine learning for root cause analysis can help identify the event that caused the change quickly and easily.
Things can sometimes go wrong in your business’s daily operations. It can be a minor issue, such as a system outage lasting for a couple of minutes. Or it can be something severe as a cyberattack.
Generally, such events result from a chain of actions that eventually culminate in the event. Identifying the root cause is the best way to solve the issue. But manual root cause analysis takes time and often doesn’t provide the exact cause of a mishap. Using machine learning for root cause analysis can automate the process, helping identify the underlying cause quickly and with higher accuracy.
Power of Machine Learning for Root Cause Analysis
To understand why an issue occurred, you need to identify the root cause. But root cause analysis can often be complex and provide inaccurate results. Using machine learning for root cause analysis helps solve this issue.
Using machine learning for root cause analysis can help zero in on the exact location of the problem. You don’t have to scroll through infinite logs to identify which components were impacted and when. The machine learning program can automatically and quickly find the root cause by analyzing a given log data set.
Moreover, the machine learning program can even predict future incidents as more and more data is available. The program compares real-time data with historical data to predict future outcomes and warns you of any unwanted incident beforehand. This will help improve your incident response, reduce downtime and improve productivity.
Benefits of Using Machine Learning for Root Cause Analysis
There are many benefits of using machine learning for root cause analysis. It can help teams take the right action at the right time, minimizing your losses. Some of the benefits are discussed below.
The cost of solving the issue is reduced as your teams don’t have to guess and work around blind spots. Machine learning tools locate the exact line of code responsible for a performance issue, and your team can start working on fixing it right away.
The time spent fixing the issue is significantly reduced as it helps solve business pain faster by locating the cause quickly and accurately.
Provides Long-Lasting Solutions
Machine learning tools provide a permanent solution for your problems and foster a productive and proactive approach.
Grows Your Business
Using machine learning for root cause analysis helps improve the efficiency and productivity of your organization, which eventually leads to business growth.
No system is perfect. Incidents will happen, no matter what. But what you do afterward is in your control. Root cause analysis should be done as soon as possible. Using machine learning for root cause analysis not only improves your incident response, but over time, it can also help prevent incidents from happening in the first place.
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