January 14, 2009
Using XAM with Nearline 2.0 to Ensure Data Compliance
Recently, SAND has been conducting tests on our SAND/DNA Access product to benchmark its support for the XAM (eXtensible Access Method) API. These tests, executed at EMC’s lab in Hopkinton, Mass. using the latest version of EMC’s Centera solution, were a great success in a number of respects, and the SAND/DNA Access Nearline 2.0 software component is now the first commercial product to obtain XAM certification from EMC. In today’s blog post, I will describe the XAM interface, explain our motivation for implementing it, and provide some details about the benchmark tests.
November 10, 2008
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.
October 27, 2008
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.
October 20, 2008
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.
October 13, 2008
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. Read more…
October 6, 2008
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. Read more…
September 23, 2008
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.
August 13, 2008
Intelligent Information Management Part 2
In my previous post, I quickly introduced the concept of Intelligent Information Management. In today’s post, I discuss Information Lifecycle Management (ILM). ILM is one component of IIM best practices, dealing with the management of data from the moment of its creation up to its disposal. Studies have demonstrated that the rate of access for a given data set drops dramatically after 90 days. In fact some studies claim that currently less than 30% of the data in an enterprise data warehouse (EDW) is actively accessed by users. However, some organizations are responding to data retention regulations by storing data that is accessed very rarely in the data warehouse “just in case”, causing unnecessary database growth and increased TCO. These organizations are essentially using the EDW as a storage device – a very expensive one indeed!
July 18, 2008
Intelligent Information Management Part 1
Summer is well underway here in Montreal, bringing with it blue skies and warm temperatures. This is an exciting development, especially since the last winter was very long –- just like the amount of space I could devote to the topic of today’s post from the bus! There are many different aspects of Intelligent Information Management (IIM) to be discussed, and the subject is just too important to be dealt with in a hurry, so I will be covering this topic in multiple posts.
June 18, 2008
Columnar Deduplication and Column Tokenization: Improving Database Performance, Security, and Interoperability
For some time now, a special technique called columnar deduplication has been implemented by a number of commercially available relational database management systems. In today’s blog post, I discuss the nature and benefits of this technique, which I will refer to as column tokenization for reasons that will become evident.