Only 4 % attribute better decision making to #bigdata.

I recommend you this article (link below). It’s a realistic opinion with a slightly pessimistic tone (because of examples of perished hopes) on effective utilizing big data science in business.

Earth be still.  Big data has lost its luster. Could it be that analyzing terabytes, exabytes and zettabytes of information won’t make us smarter … or, even worse, could it make us wrong? We’re beginning to see headlines like “Google and the flu: how big data will help us make gigantic mistakes” in the Guardian, “Eight (No, Nine!) Problems With Big Data” in the New York Times and “Big Data: Are we making a big mistake?” on author and Financial Times columnist’s Tim Harford’s site.


I found out today about thanks to the job offer which arrived on my LinkedIn mailbox from Tania L. It’s a website which reminded me in the beginning of a oDesk alternative, but it makes for more direct connection on the line between engineers and employers. The are no job offer listing here, toptal makes sure that a company will find proper engineers for its ongoing tasks.

At Toptal, you won’t have to throw product requirements over a wall, deal with multiple middlemen managing your team, or pay a flat project fee. Instead, you will be connected with a superb remote engineer, who will interface with your team as an internal engineer working from home would.


It’s another example for me when a company focused on analyzing and dealing with job market searches for a Data Scientist. Recently I saw Data science internships in the Monster co. (yup, the job search portal). So despite the recession job seek professions seems fine and they have a lot of multidimensional data to analyze.

What got my attention is that they publish open source to GitHub profile:

Not to shabby!

Where to put your scientific big data for sharing online?

Well, first of all, the big data used by researches must be given from hand to hand (this ad-hoc is the worst solution 🙂 ) to enable cooperation, teamwork etc.

Secondly, research financial grants often requires that data and code must be open-source and available freely.

Cloud computing of course is not always possible, and that’s the reason of creating this post. Researches use vast majority of statistical and data-mining tools which operate on local resources or at most remote databases (i.e. RapidMiner, R).

Dropbox and public folder – especially good solution if you have gained a lot of extra space because of limited promotions (i.e. after buying a Samsung smartphone).  I had around 100GB in a peak moment and was called a Dropbox Guru twice. After putting data into “Public” folder, you have there a thing in right-click-menu called” copy public link” which allows to download data anywhere from the world.

Amazon AWS cloud – popular within companies, but solution payable depending on the transfer used

Google Cloud Storageinfo at Google Developers

Rackspace Cloud Storage, Cloud CDN and Unlimited Online Storage by Rackspace

MS Azure Cloud Servicesinfo on Windows Azure

Amazon Personal cloud

Google Drive


Dedicated machine with cloud software – i.e. Synology NAS drives

Reasonable (only in case of lack of money) seems to me to create couple of cloud accounts and use them to put data after after separating it into multiple parts (depending on the size they have).