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Learn to help your teams own their unstructured data with tagging, assigning datasets, and custom reporting in StorageMAP.

Orphaned data can add huge levels of risk to a business. Learn how StorageMap reduces unstructured data management risk by taking control of orphaned data.

Datadobi StorageMAP 6.3 lets you copy file data to any S3-compatible object storage system providing ESG compliance and maximizing the value of unstructured data.

Your company’s data can consume a huge amount of energy, even if you use the cloud. Learn how StorageMAP manages unstructured data storage and reduces your carbon footprint.

Orphaned Data Risk and Cost Can Now Be Eliminated with Multi-Vendor, Multi-Cloud Unstructured Data Management Platform. Pushing orphaned data to the back burner – i.e., data that still sits on an organization’s network or systems but was created and owned by a now deactivated employee – is never a good idea. Yet, we work with some…

Datadobi Query Language (DQL) is a query framework that identifies unstructured data within a data lake, giving your DevOps team the ability to script intricate queries for every data mining task.

Climate change is no longer a distant threat for business leaders. Because of this, Environmental, Social, and Governance (ESG) policies are now high on the agenda of almost every major corporation in the world.

In some of my most recent blog posts, I discussed how the top unstructured data concerns for enterprises today include maximizing scarce data storage resources, controlling carbon footprint, reducing data risk, and extracting value from data.

Learn about the difference between a snapshot and copies and how Datadobi’s StorageMAP solution can simplify data migration from one enterprise NAS to another.

In the last several years, many customers have shared a frustratingly common secret about their file and object unstructured data. Prior to contacting Datadobi, most of them had been operating with limited knowledge of what types of unstructured data they have, where it is, and even how much they’re consuming on-premises or in the cloud.

Expanding on the core migration and data protection offerings, Datadobi has introduced StorageMAP, bringing full unstructured data management capabilities to enterprises with large and growing data estates. But what is “unstructured data management”? In a nutshell, it is combining the ability to have a full view of all the file and/or object data in your organization combined with tagging and the ability to take action on any dataset of interest.

Back in the spring of 2020, Dell Technologies changed the face of enterprise storage with the introduction of Dell® PowerStore®.  PowerStore’s revolutionary software-driven architecture has since helped customers around the world adapt to any future, transforming both traditional and modern workloads with new flexibility, efficiency, and performance. 

Unstructured data environments can be riddled with chaos. Masses of redundant, obsolete, or trivial (ROT) data idling on a network, the sheer volume of unstructured data, and heterogeneous storage structures all contribute to headaches for IT leaders.

Complicating matters even more is that every storage environment has a unique set of unstructured data standards. In our multi-vendor world, relying on native unstructured data management features from each storage vendor or cloud provider won’t cut it anymore.

By now it is common knowledge that unstructured data is growing out of control. So the question everyone is asking is, how do you truly manage unstructured data in the multi-vendor, multi-cloud environments that are becoming the norm?

There is no shortage of companies, from one end of the storage industry to the other, claiming to clean up the toxic landfill that has become unstructured data. But how, as an overworked under-budgeted IT professional, do you quickly understand the benefits of each approach?

As almost anyone with IT experience knows, managing an unstructured data environment can be challenging in so many ways. There are numerous factors that must be considered, including but not limited to: the number of storage devices, size of your dataset, number of files in your dataset, and maybe the worst of all – the uniqueness of your environment. This is because one-size-fits-all solutions are oftentimes not a great fit, and instead, an ad hoc management style is adopted leading to a potential future hurdle referred to as “tribal knowledge.”