My first links about this topic:
Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to “spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.”
As of 2012, limits on the size of data sets that are feasible to process in a reasonable amount of time were on the order of exabytes of data. Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research. The limitations also affect Internet search, finance andbusiness informatics. Data sets grow in size in part because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio-frequency identification readers, andwireless sensor networks. The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data were created. The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.
Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead “massively parallel software running on tens, hundreds, or even thousands of servers”. What is considered “big data” varies depending on the capabilities of the organization managing the set, and on the capabilities of the applications that are traditionally used to process and analyze the data set in its domain. “For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.”
What is big data?
El desafío del “big data”, más que sólo grandes volúmenes de datos
Big data: The next frontier for innovation, competition, and productivity
Data Science Summit
Big Data: Evolution or Revolution?
DataSift Using MySQL, HBase, Memcached to Deal With Twitter Firehose
DataSift Architecture: Realtime Datamining At 120,000 Tweets Per Second
Explaining Hadoop to Your CEO
The World’s Technological Capacity to Store, Communicate, and Compute Information
The Big Data Boom Is the Innovation Story of Our Time
MongoDB Intro & Application for Big Data
The Big Data Bottleneck In The Consumer Web
Microsoft drops Dryad; puts its big-data bets on Hadoop
Distributed Cache as a NoSQL Data Store?
Building Scalable Systems: an Asynchronous Approach
Big Data Intelligence on Hadoop
Ville Tuulos on Big Data and Map/Reduce in Erlang and Python with Disco
The elephant in the room … Hadoop and BigData!
Is Microsoft’s Future in Data-as-a-Service?
Resolving the contradictions between web services, clouds, and open source
Strata Gems: Where to find data
Big crime meets big data
Data and social media are being used against us in creative new ways.
Tech Talk: Nathan Marz — “Clojure at BackType”
Do We Need a New Programming Language for Big Data?
Angel “Java” Lopez