Nearline 2.0 and the Data Management Framework
In my last post, I outlined some of the advanced data modeling options that have been made possible by the advent of Nearline 2.0. Today, I want to discuss how Nearline 2.0 can act as an essential component in a data management framework. The data management framework, which can be viewed as an extension of the data model concept to the level of enterprise data architecture, governs the processing and management of enterprise data throughout its “lifecycle”, from creation to disposal. It includes all operational components, and covers key issues such as data backup, disaster recovery, data retention, data access security and so on.
Nearline 2.0 and Advanced Data Modeling
In my last post, I discussed the “Quick Check” method for identifying the benefits that a Nearline 2.0 implementation can deliver in the areas of operational performance, SLAs and TCO. Certainly, it is easy to see how it would be preferable to manage a database that is 1 TB rather than 20 TB in size, particularly when it comes to critical tasks like backup and recovery, disaster recovery, off-site backups and historical analytics. Today, however, I want to focus on another benefit of Nearline 2.0 that is less obvious but still very important: data modeling flexibility.
Starting Nearline 2.0: The Quick Check Approach
In previous posts, I introduced the concept of “Best Practices” for Nearline 2.0. Today, I will get down to the details of how and where to start with a Nearline 2.0 solution, beginning with a Best Practices approach designed to quickly identify the benefits of such an implementation in a given environment. At SAND Technology, we offer this “Nearline 2.0 Quick Check” as part of our professional services portfolio.
Nearline 2.0 Best Practices
In previous posts, we introduced the concept of Nearline 2.0, showed how it represented a significant step forward from traditional archiving practices, and discussed how Nearline 2.0 could help your business. To recapitulate: the major advantage of Nearline 2.0 is its superior data access performance, which enables a more aggressive approach to migrating data out of the online repository to nearline (a process known as “data nearlining”) than is practical when using a traditional archiving product.
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How Can a Nearline 2.0 Solution Help Your Business?
In my last post, I discussed how a Nearline 2.0 solution allows vast amounts of detail data to be accessed at speeds that rival the per-formance of online systems, which in turn gives business analysts the power to assess and fine-tune important business initiatives on the basis of actual historical facts. We saw that the promise of Nearline 2.0 is basically to give you all the data you want, when and how you want it — without compromising the performance of existing warehouse reporting systems.
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Nearline 2.0 vs. the Archive
In his most recent SAND blog post, Richard introduced the notion of “Nearline 2.0” and discussed how this concept, and related best practices, can be of vital importance to businesses dealing with the “data tsunami” we’ve been experiencing in recent years.
In this post, I’d like to step back a moment and explore the ways in which the dynamics of Nearline 2.0 differ from traditional methods of data archiving in terms of their approach to keeping data warehouse size under control.
Introducing Nearline 2.0
In today’s post, I want to introduce the notion of “Nearline 2.0″. While the name might seem esoteric, this concept represents the logical evolution of older data warehouse and information lifecycle approaches that have struggled to maintain acceptable performance levels in the face of the increasingly intense “data tsunami” that looms over today’s business world. Whereas older archiving solutions based their viability on the declining prices of hardware and storage, and rigid “Nearline 1.0” solutions were primarily designed to work with transactional systems, Nearline 2.0 embraces the dynamism of a software and services approach to fully leverage the potential of large enterprise data architectures.