In our last post we looked at the elements making up data aware storage.  In this post we'll look at a data-aware storage feature that is unique to HubStor: data aware policies.

In our view, data aware storage needs to be more than just insight into IOPS and item counts. And data governance is increasingly important today. A data aware policy engine leverages the underlying data-aware storage framework and connects meaningful insights with data governance use cases of security, eDiscovery, and IT.

EXCLUSIVE BONUS:  Download our Feature Brief on Data Aware Storage in the Cloud for an in-depth look at the ways data awareness boosts security, eDiscovery, and storage management.

The Policy Engine

HubStor's data aware policy engine operates on a near-real time bases.  Data aware policies can be run in an analytics-only mode -- to model a policy, to provide tracking, or simply for purposes of reporting -- or they can be used for executing actual rules like tagging, indexing, retention, etc.

What Makes a Policy Data Aware?

The policies are data aware because:

1)     Data Aware Storage Criteria - policies can leverage the flexible object storage metadata model of Stors (which we detailed in our last post), as well as user awareness, so that any metadata, tag, or identity can be used to scope policies, and

2)     Integrated Analytics - policies automatically maintain their own analytics so that, at any time, you have up-to-date insight of the history and coverage of each policy.

Why Data Aware Policies Matter

We all need to sharpen the pencil on data governance and security.  Data aware policies is how we do it.

In traditional archive and content management systems, policies weren't something you could model and they weren't easily reported on.

What's exciting about data aware policies is that they break through both these barriers -- providing on-demand visualization of the scope and history of each policy, and making it quite simple to model a policy with rich analysis before executing it.

Data aware policies are also designed to work at scale - with policies tracking data sets of a billion items or more.  At such large scale, analytics are maintained in an optimized design so that insights up-to-date, readily available, and the system resources are used efficiently.

Conclusion

Data aware storage should be more than just insight into IOPS and item counts.

Real value is when data aware storage insights are integrated in a meaningful way with the actual use cases of security, eDiscovery, and IT.  The data aware policy engine is what bridges the underlying intelligence maintained in the storage layer with the data governance apps.

In our next posts in this series on data aware storage we'll go into use case examples, showing you how data aware storage benefits the business in various ways.