Gary King, director of Harvard’s Institute for Quantitative Social Science for The New York Times:

“It’s a revolution. We’re really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government. There is no area that is going to be untouched.”

Original title and link: It’s a revolution - The Impact of Big Data in the World (NoSQL database©myNoSQL)

via: http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html?_r=2&pagewanted=all

An ArsTechnica, not very technical, overview of the storage engines developed and used by Google (Google File System, BigTable), Amazon (Dynamo), Microsoft (Azure DFS), plus the Hadoop Distributed File System (HDFS).

Original title and link: How Web giants store big data (NoSQL database©myNoSQL)

via: http://arstechnica.com/business/news/2012/01/the-big-disk-drive-in-the-sky-how-the-giants-of-the-web-store-big-data.ars/1

The story of adopting Hadoop (through Zettaset) at Zions Bancorporation:

The quest for a solution began in 2009 with an investigation of Zion’s existing Microsoft and Oracle technologies, as well as other technologies within the firm and new solutions on the market, Wood relates. After developing a list of six potential vendors, he says, he and his team quickly focused on two Hadoop-based solutions. The team, Wood explains, recognized the potential in Hadoop for “making security decisions proactively rather than reactively, based on mining business intelligence and combining it with event data from security devices.”

Original title and link: The Outer Limits of Data Warehouse Technology (NoSQL database©myNoSQL)

via: http://www.banktech.com/business-intelligence/232600226?printer_friendly=this-page

The MapR team analyzes the top 5 misconceptions in the Big Data/Hadoop market:

1. Big Data is not simply about massive amounts of data — petabytes and beyond. Big Data represents a paradigm shift.
2. Since Hadoop is a funny name and somewhat new to people they assume it must be risky.
3. Another misconception about Hadoop, is that it is a batch process.
4. Perhaps the biggest misconception is that Hadoop is a single, monolithic, component.
5. With respect to open source, the question about a distribution is not a simple binary “open” or “closed”.

The first 4 points are indeed how things are seen from the outside.

While I do understand the nuance introduced by the last point—allowing to plug MapR—, things are black and white: it is either open source or not. But that’s just one dimension of the various components of the Hadoop stack. What really matters is how well a component integrates with the rest of the stack. The questions to be asked are: does it maintain the same interfaces? what’s the cost of replacing it? does it allow to use a 3rd party component? does it force me to get special components or hardware?

Original title and link: 5 Top Misconceptions about Big Data and Hadoop (NoSQL database©myNoSQL)

via: http://www.mapr.com/blog/top-misconceptions-about-big-data-and-hadoop

Put together one of the most impressive visualization tools, Tableau Software, with one of the best solutions for big data, Hadoop, and you’ll probably get some astonishing results.

Credit Cloudera.

While Tableau Software works with structured data only, with this connector it gets access to Hive through HiveQL.

Original title and link: Visualizing Hadoop data with Tableau Software and Cloudera Connector for Tableau (NoSQL database©myNoSQL)

via: http://www.cloudera.com/blog/2012/02/cloudera-connector-for-tableau-has-been-released/

Roger Ehrenberg (Founder and Managing Partner of IA Ventures):

Every so often a term becomes so beloved by media that it moves from “instructive” to “hackneyed” to “worthless,” and Big Data is one of those terms. […] But since this time the term Big Data has become diluted. Very diluted. So much so that it is almost totally meaningless. Does Big Data mean new kinds of databases? Sure. Does it mean innovative ways to visualize data to create actionable intelligence? Absolutely. Can it be applied to the health care sector? Without question. Has it contributed to the rise of the Data Scientist? Mos def.

And I thought I was off.

Original title and link: What’s the big deal about Big Data? (NoSQL database©myNoSQL)

via: http://informationarbitrage.com/post/16121669634/whats-the-big-deal-about-big-data

Brian Christian¹ (Zettaset) suggests two roads for adopting Hadoop:

The first, building the capability internally, seems to hold out the promise of flexibility and control for organizations that employ it. While this has sometimes been the case for some large companies, a variety of studies indicate that even among Fortune 500 companies, less than 20 percent that began Hadoop development succeeded in deploying a solution.

The second approach entails working with a big-data, Hadoop-focused third party to develop a bespoke solution. In addition to eliminating the requirement of enormous equipment and human capital investment, this approach also enables organizations, their executives, and IT staff to focus on their core value propositions rather than being forced to become Hadoop specialists.

It would be easy if the decision what be just about CAPEX vs OPEX. Or on-premise vs managed deployments. But there are tons of variables that must be considered when going the Big Data way. Eventually pretty much everyone will do something around Big Data, but those at the forefront still have to figure out many important aspects.

Original title and link: How to Hadoop: Maximizing the value of big data (NoSQL database©myNoSQL)

via: http://venturebeat.com/2012/01/24/big-data-server-efficiency/