Big Data. Big Decisions
InformationWeek
Special Coverage Series

Commentary

George Crump

Storage Software Vs. Hardware: What's More Important?

Open source software and off-the-shelf hardware both play a role in the commoditization of storage. Consider these 3 questions to understand the issues.

A common theme from storage software vendors over the last few years has been that storage hardware is becoming commoditized and that it is the software that really matters, not the hardware. It is a fair point. Entire companies have been built on the value that the software brings to off-the-shelf hardware. However, a recent trend has been for storage hardware companies to claim that it is the software that is becoming commoditized, not the hardware.

1. What Is Storage Software?

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

Storage software is the software that makes a bunch of disk drives act like a system. At its most basic level the storage software provides volume management, RAID protection, and LUN masking. Vendors advanced these capabilities significantly over the years and added features like snapshots, thin provisioning, replication, and clones. Most recently they have been adding some form of SSD automation via tiering or caching.

2. How Can Storage Software Become A Commodity?

Storage software can become a commodity by becoming so commonplace that it is included automatically with the operating system, file system, or hypervisor. Look at the capabilities of the open storage software products like ZFS, GPFS, GFS, MogileFS, Lustre, Nexenta, Datacore, Glustre, and Caringo (to name a few) and compare them with some the capabilities from turnkey storage vendors and you will be surprised at the capabilities of these products.

At first glance you may think that this bolsters the argument that software solutions will make the storage hardware a commodity, until you realize that many of the above software solutions are either open source or very aggressively priced. Once something is available for free, something that will never happen to hardware, then it is by definition commoditized.

3. Does Storage Hardware Suddenly Matter?

There are three key drivers to why storage hardware suddenly matters. The first driver is being caused by flash memory. How vendors integrate flash will directly impact your experience with the system. As we discuss in our recent video "The SSD Price Problem" flash storage is not all created equally and the actual flash NAND is one small component of the overall flash solution. A more vertically integrated solution may deliver better performance and density at a better price point.

The second driver is the network. As the performance of storage systems begin to scale, the cost and complexity of the storage network become an issue. As we discuss in our article "In Open Storage The Storage Infrastructure Matters," some storage hardware vendors are pre-integrating low-cost networking options into their storage offerings so that the cost of the storage network does not become greater than the storage itself.

Finally there is reliability. We repeatedly see evidence in our labs and in talking with customers that certain vendors deliver higher levels of reliability than others. They accomplish these higher levels of reliability not only by better testing but also better design. As we discuss in our article "The Requirements for Building Reliable Storage Systems," better storage hardware designs can reduce vibration and increase air flow so that drives run cooler. Vibration and heat tend to be the top killers of hard drives.

The end result is that both storage hardware and software are being commoditized at different levels. There are plenty of systems available that are really software leveraging off-the-shelf hardware, there are systems with hardware that can leverage a variety of software, and there are systems that have commoditized everything (hardware and software).

What makes the most sense for your data center depends largely on how much time and motivation you have. The more commoditized approach you go, the more assembly that is required. It will save you money, but may cost you time.



Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.