Introduction The term Big Data is gaining more followers and popularity. However, despite this trend, not all organizations are clear about how to face the challenge to store, organize, display and analyze large volumes of data. The term Big Data is gaining more followers and popularity. However, despite this trend so evident, not all organizations are clear about how to face the challenge to store, organize, display and analyze large volumes of data. There are multiple techniques in terms of huge database storing approaches that can store petabytes, exabytes and may be zetabytes data. These options are Cassendara, Mongodb and HBase. We will discuss about them one by one and in a proper research method and will compare them in order to contrast their difference and efficiency.
Research Background
One problem in understanding the phenomenon is that the size of these data sets the volume greatly exceeds the Data warehouse. A plane collects 10 terabytes of information from sensors every 30 minutes flight, while the Stock Exchange of New York collects structured information 1 TB per day.
In the context of Big Data, volumes are reaching peta bytes, exa bytes and then soon to zeta bytes. For instance, Apple has just announced that 7 trillion send daily notifications to iOS devices. The explosion of information in social networks, blogs, and emails is characterized the presence of data key "unstructured" and "semi" in contrast with the data type “structured” is what is commonly handled in the Data warehouse.
However, the concept of Big Data makes sense from the moment that not only the volume but also the speed and variety of data exceeds the processing capacity that can handle traditional IT systems into information of value to decision. This last feature, the value is the key
