What is Big Data ?
We live in a
post-crisis world, in which we have a gradual economic recovery in
the United States and major investment from China. A world that is
increasingly globalized and increasingly digital, in which the
algorithms controlling transactions on stock exchanges and a simple
browse at a website can generate data on personal preferences,
guiding product manufacturing and service settings. Unstructured
data, sourced from Facebook posts, tweets, YouTube videos, search
results, geolocation among others, represent approximately 80% of the
data that companies need to deal today.
From these mountains of complex data that are generated and stored daily from heterogeneous sources, that needed to be analyzed in the shortest possible time to reach its final value, the market coined the term "Big Data". Big Data involves 3 Vs: Volume (high volume bulk data), Variety (variety in the types of data) and Velocity (speed of processing time required for business).
According to one definition of Wikipedia (http://en.wikipedia.org/wiki/Big_data), Big Data involves the use of large data sets from different sources, which can be structured or not, by whose today's processing tools of database management systems and applications often become a complex job . The challenges of dealing with these large volumes include capture, store, search, search, analyze, share and view this data in the given time and needed by the business. The sizes of these data sets ranging from tens of terabytes to many petabytes.
For the Gartner Group, Big data involves information assets with high volumes, velocity and variability, which require innovative ways of processing information to obtain the best results of analyzes that can reflect on the decision process.
The traditional approach to data storage and simple clustering in another node can not get the performance needed to achieve full readings and analyzes these massive data sets. To meet these new challenges, new software platforms and new techniques had to be developed. According to research conducted by 2015 the Big Data market is expected to grow 40% per year.
From these mountains of complex data that are generated and stored daily from heterogeneous sources, that needed to be analyzed in the shortest possible time to reach its final value, the market coined the term "Big Data". Big Data involves 3 Vs: Volume (high volume bulk data), Variety (variety in the types of data) and Velocity (speed of processing time required for business).
According to one definition of Wikipedia (http://en.wikipedia.org/wiki/Big_data), Big Data involves the use of large data sets from different sources, which can be structured or not, by whose today's processing tools of database management systems and applications often become a complex job . The challenges of dealing with these large volumes include capture, store, search, search, analyze, share and view this data in the given time and needed by the business. The sizes of these data sets ranging from tens of terabytes to many petabytes.
For the Gartner Group, Big data involves information assets with high volumes, velocity and variability, which require innovative ways of processing information to obtain the best results of analyzes that can reflect on the decision process.
The traditional approach to data storage and simple clustering in another node can not get the performance needed to achieve full readings and analyzes these massive data sets. To meet these new challenges, new software platforms and new techniques had to be developed. According to research conducted by 2015 the Big Data market is expected to grow 40% per year.
In the
next post i will talk about Big Data in the Oil & Gas industry.
Later on we are going to talk about Hadoop, MapReduce, HDFS, etc.
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