Big Data. Big Decisions
InformationWeek
Special Coverage Series

Commentary

Doug Henschen

Doug Henschen

Executive Editor, InformationWeek

AOL Taps Vertica Database

Column-store platform edges out IBM Netezza, Oracle and others to provide self-service data sets for advertizing optimization and internal traffic analysis.

"We've got data, and we're not afraid to use it."

This is how AOL describes the mission of its "armies of researchers, engineers and other thick-framed-glasses types" who deliver sought-after online audiences. To let customers and internal business users do more of this work on their own, AOL has added Vertica to its portfolio of data warehousing tools, it was announced on Tuesday.

Vertica was selected to replace MySQL as the database of choice for delivering online data sets and reporting applications. As these data sets have grown, the company was running into performance problems including data sharding and difficulty replicating information across multiple servers, according to Mark Ettrich, senior technical director at AOL.

"Vertica will help us support robust reporting and data consumption whether it's through front-end Web services, APIs or direct SQL queries against Vertica boxes," Ettrich told InformationWeek.

If your last frame of reference on AOL was the 1998 romantic comedy "You've Got Mail" or even the subsequent flame out and divorce from Time Warner in 2009, it might surprise you to learn that AOL's Advertising.com is said to be the number in network in online advertising, reaching 74 out of comScore's top 100 Web sites. AOL has also morphed from a dial-up Internet access provider into an original content publisher, with rising sites including Engadget, Politics Daily, AOL Music and Black Voices.

AOL will deploy Vertica over the coming year to power both external and internal self-service query and reporting applications, Ettrich said. AOL Advertising and Advertizing.com customers, for example, want to measure brand awareness and analyze the success of advertising campaigns. Internal customers might want to examine traffic sources and trends, or revenue scenarios.

Vertica's column-store database supports massively parallel processing (MPP) on commodity hardware. The column-store approach -- an architecture shared by competitors Sybase IQ and ParAccel -- maximizes data compression and speeds analytic queries that typically interrogate only selected dimensions across rows of data. MPP further enhances performance by spreading workloads across tens, hundreds or even thousands of nodes.

AOL was already using (and continues to use) Oracle and Netezza for data warehousing, but the company considered products from Aster Data, Greenplum, Oracle, Netezza and Vertica in the MySQL-replacement evaluation.

AOL's requirements included the ability to serve up multiple 10-to-15 terabyte data stores with continuous data loading for near-real-time reporting. The company also tested the ability to support at least 300 concurrent users.

"One of the stand-out features of Vertica that appealed to me was the ability to run on true commodity hardware," Ettrich said. "Some vendors say they run on commodity hardware, but they stipulate particular configurations, interfaces and configurations to get optimum performance."

AOL uses reverse auctions to purchase its hardware, and it specs out several commodity (White) boxes that can be used to run the Vertica database with stand-out performance, Ettrich said.

Asked if AOL intends to consolidate data warehousing platforms, Ettrich demurred, "I don't want to make any statements about that at this point."

Founded in 2005, Vertica was among a handful of upstart analytic database providers that emerged and made inroads against data warehousing incumbents Oracle, IBM, Microsoft and Teradata in recent years. Consolidation in 2010 saw two early pioneers, Netezza and Greenplum, acquired by IBM and EMC, respectively. Aster Data, InfoBright, Kognitio, ParAccel and Vertica are among the independents remaining.

AOL raises Vertica's customer count to 328 firms, according to the company, and it lists Bank of America, BlueCross BlueShield, Comcast, Sunoco, and Verizon among other prominent customers.



Related Reading


More Insights




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.