Big Data. Big Decisions
InformationWeek
Special Coverage Series

Commentary

George Crump

Data Tiering By Storage Location

Tiering systems in the future will move data within storage systems, to different storage systems, and even into the server hosting the application.

As we discussed in our last entry "Will Solid State Kill Tiering," the need to move data between different types of storage will increase as memory-based storage becomes more prevalent. Very soon, tiering will not only mean moving data within the storage system itself, it will also mean moving data to different storage systems and even into the server hosting the application.

With mechanical hard drives, the bottleneck created by the storage networking infrastructure was not apparent because the latency of the drives themselves. Memory-based storage has no latency and therefore the bottleneck is exposed. This in large part explains the success of PCIe-based solid state storage devices. PCIe solid state devices should've faced an uphill battle as they went against the conventional wisdom of shared storage.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

Instead, these components have seen wide adoption because of the cost effectiveness, simplicity to install, and raw performance. As we discussed in our article "What is Storage Class Memory," vendors have been successful at positioning PCIe-based solid state storage devices as a second tier of memory instead of a faster tier of storage. This is because of its near zero-latency performance since it is only separated from the CPU by the PCIe channel.

There is also a storage opportunity with PCIe-based solid state storage. The problem with PCIe-based solid state as storage is that it does create a separate tier of storage, one that is not only different than the mechanical hard drive but one that also is in a different physical location than the shared storage system that typically houses the mechanical hard drive. Automated tiering and caching systems will be the answer to these problems as they become location aware.

Today, we already have separate caching solutions being deployed in servers, leveraging PCIe solid state in parallel with solid state in shared storage. This allows for extremely active data to be cached on solid state storage inside the server and off of the network. With these configurations, active "read" data is stored inside the server--which means less data needs to transfer back and forth across the storage network. Implementing this type of technology could be an alternative to upgrading to the next faster network.

The challenge with these systems is that there is no orchestration of any kind since the caching or automated tiering software is unaware of each other. If the server-based solid state storage is used as a read cache, then data safety should be high and performance should certainly improve. But it will not be optimal. In the future, there needs to be some coordination between the location of the two high-speed storage devices so that maximum performance can be achieved. Something we will explore in greater detail in our next entry.



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.