Big Data. Big Decisions
InformationWeek
Special Coverage Series


The Small Revolution Of The Data Center

Financial services firms are rethinking how--and why--they build data centers, turning traditional data center models on their heads.

Analytics Slideshow: 2010 Data Center Operational Trends Report
Analytics Slideshow: Data Center Operational Trends Report
(click for larger image and for full slideshow)
A small revolution is taking place in the world of data centers. As financial firms search for more-effective ways to process their workloads, they have been rethinking how--and why--they build data centers.

For firms like JP Morgan, that has meant turning old computing models upside-down. "In the legacy model, you used to build an application and you would create the infrastructure to accommodate it," says Peter Ahrens, managing director and chief technology officer at New York-based JP Morgan. "So the applications themselves created the infrastructure design."

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

In the new model, the bank builds the infrastructure, and the application is designed to fit the existing environment, Ahrens explains. "The application is less customized, but it's available on demand. And it's cheaper," he adds.

One of the biggest challenges firms traditionally face when they plan a new multimillion dollar data center is that the facility often represents a 10-year investment, and they must try to envision their power, server and storage needs for the next decade before they start building. But with technologies and storage and data requirements evolving so rapidly, current needs may differ wildly from what a firm will require in just a few years.

"We would historically have built or provisioned data centers on the basis of things we knew at one point in time," Ahrens relates, adding that engineers would have planned for a specific amount of rack space, storage and cooling, and electric distribution would have been consistent with what they knew at that time. "But today," he says, "some dynamics are changing."

And they are changing more and more rapidly. For example, the overall mix between the number of servers and storage capacity has shifted, Ahrens points out. The power of servers today means firms can run fewer in the data center than historically, he says. Meanwhile, use of storage has been multiplying. "Five to eight years ago, you would have had a small number of storage devices and a large number of server racks," he comments. "Today the trend is toward more space consumed by storage and less by server racks."

Other traditional assumptions about the data center that have been turned on their heads include power requirements. "Some new technologies require far more power," Ahrens says. "Even if you have room on the floor, you may [no longer] have adequate power or cooling to run devices in that location."

While JP Morgan, like most major Wall Street firms, would once have built large facilities according to its expected future requirements, it is now building more-flexible data centers in what Ahrens calls a "pod-like" fashion. "In a couple of recent acquisitions, we built a large facility and put a large shell over it," he says, explaining that the bank builds out one pod, or room, in the facility at a time. "We're trying to match up our investment stream more closely to when we need it--in other words, to only spend money when we need it.

"Our business drivers are, 'Don't spend money and build stuff until you need it,'" he continues. "And by doing that, you are better able to build the pods to the specs of what you require." For example, as JP Morgan builds out a current pod, the firm's engineers might focus on the fact that power density and floor loading will need to be designed more for storage needs rather than server requirements, Ahrens explains.

In addition, like other capital markets firms, JP Morgan has been busily consolidating its existing facilities. "We are reducing the number of data centers we have. And we will be building larger facilities with more expansion capability, rather than having many facilities scattered around the place," Ahrens reports.

Read the rest of this article on Wall Street & Technology.

IT is caught in a squeeze between requests for new applications, services, and device support and demands from upper management to keep budgets lean, staffing light, and operations tight. These are irreconcilable objectives as long as we spend the vast majority of our resources on legacy services. Read our report now. (Free registration required.)



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.