Data Warehousing
News & Blogs
Flexible Appliances: Conundrum, Oxymoron, or Opportunity?
Greenplum Blog
January 30, 2013 –
Some argue that the term “Flexible Appliance” is an oxymoron, often with good reason. Among analytics users, the emergence of preconfigured analytics appliances has been a mixed blessing. While they're simpler to deploy than build-it-yourself MPP clusters, many appliances force users to trade-off flexibility against simplicity.
TDWI
January 24, 2013 –
Ignore the hype surrounding big data. What's really important is to learn about the new models for data processing that big data is bringing so you can plan rather than react.
Forrester: Enterprises Must Look To Web For Mobile Success
ReadWrite Mobile
January 23, 2013 –
As Forrester analysts Jeffrey Hammond and Julie Ask highlight in a new report, "The Future Of Mobile Application Development," enterprises must embrace mobile to succeed, with mobile development requiring modern development techniques and technologies like elastic infrastructure, open source and DevOps. With over one billion smartphones globally, a number that keeps booming, they argue "We’re entering a new age of application development that creates modern, compelling systems of engagement and links them with systems of record and systems of operation."
Mining Electronic Records for Revealing Health Data
The New York Times
January 15, 2013 –
Over the past decade, nudged by new federal regulations, hospitals and medical offices around the country have been converting scribbled doctors’ notes to electronic records. Although the chief goal has been to improve efficiency and cut costs, a disappointing report published last week by the RAND Corp. found that electronic health records actually may be raising the nation’s medical bills.
testing the accuracy of query optimizers
Theory in Practice
January 9, 2013 –
A while back I had a very interesting conversation with Jack an application developer for a larger software company in the area; so to speak a person on the other side of the database. He maintains that database application programmers who have to support a number of database systems have long developed a kind of taxonomy of query optimizers: they know which systems have ‘good’ and which have ‘bad’ optimizers. They know on which systems queries need “tuning” and even to what extent. Apparently, there is at least one system that ‘gets it almost always right’ as Jack would put it, and lots that are way off by default. When asked, however, how he’d quantify these differences Jack simply shrugged: ‘It’s something you develop over time; can’t give you a number.’
7 Big Data Solutions Try To Reshape Healthcare
Information Week
January 3, 2013 –
Big data analytics is at that tipping point right now in the healthcare industry. Several vendors promise better quality of care and reduced expenditures, but evidence to support those claims is somewhat tentative. Similarly, some critics of the big data movement say healthcare providers need to squeeze all the intelligence they can from small data sets before moving on to larger projects.
How Food Genius built the ultimate test kitchen out of menu data
GigaOm
January 2, 2013 –
If you’ve ever ordered a dish off a menu, chances are it’s in Food Genius’s servers. The startup has compiled a mammoth database of menus with the goal of tracking what America is eating. In January it begins selling that data to food companies.
Big Data Revolution Will Be Led By Revolutionaries
Information Week
December 13, 2012 –
The truest and most memorable insight into IT industry dynamics I ever heard was delivered by Steve Mills, senior VP and group executive of software and systems at IBM. During a 2010 interview we were discussing the relative importance of various layers of the stack, and I asked him, "what's really driving customer technology selections: hardware, middleware or applications?"
Wallflowers of Silicon Valley Get Asked to Dance
The New York Times
December 12, 2012 –
After years of being wallflowers at Silicon Valley’s hottest tech conferences and Sean Parker’s after-parties, enterprise technology firms are now part of the “in” crowd. The flameouts of social media stocks over the last year have left venture capital firms searching for a more measured approach to investing.
CITO Research
December 11, 2012 –
We’ve lived with enterprise business intelligence (BI) for several decades now, and it’s no secret that many of us are fed up with the inability of such expensive technology to deliver the business value we need. Some vendors, such as Platfora, have keyed into this frustration by pushing the “panic button” and declaring the end to data warehouses and processes such as Extract, Transform, and Load (ETL). But such histrionics won’t really help us. Just because the ETL process is one of the reasons enterprise BI is slow doesn’t mean it’s unimportant. The idea that we won’t have to clean and disambiguate data is ludicrous.
