What happens is that the traditional data sets? They're free to handle such kind of data because they are generally made for how links, structured datasets and usually the databases which are being used is a kind of relational database and the traditional systems. They fail to handle data of such kind of huge volumes, huge variety and huge veracity. Apart from that, the data warehouses, which we have, they may not be able to handle the processing demands modelled by big datasets, because if you are going to process data sets of huge volume, even if you apply some kind of algorithms for their processing. What will happen? The algorithms would or as an intermediate result or the results which are being generated might be of a much higher volume.
Failure of Traditional Analytics Tools:
Our traditional systems, they are not developed to handle such kind of huge data sets. They might fail apart from that, If you are going to process large datasets. What you need? You need processing capabilities, which is also very large and it is able to handle huge datasets. In that case, also your traditional data warehouses, your traditional tools and technologies which are being used, they will fail.
For Example: if you have to perform some kind of real time data analytics on stock trading website, online activities or maybe you want to track down some performance of some kind of applications etc. In that case, also, you need to perform analytics on huge datasets. In order to process such kind of huge data sets, data sets of such large kind of volume.
What do you need? You need better tools, better technologies and better techniques which can handle such kind of large volume of data set.
Big data analytics best tools and Technologies which are being used, We have MapReduce which is becoming very popular and synonymous tools used in big data analytics.