In today’s world, data’s growth is a big challenge. Discussing the complete world, we are producing petabytes and terabytes of data daily. You purchase a specific item; go to a specific vacation, or you would end up generating some data. You click pictures, upload them on FB, tweet about it and go make many emails and documents online. However, every data is not accessible by us for storing and analyzing.

In terms of volume, the data is growing and we require a device, with which we not only can store the data but also can procedure the big amount of data and have some understanding out of it. So, this is where the big data technologies come into picture which helps you process the big amount of data and having some meaningful understanding out of it.

Different sources of Big Data:

There are various sources of big data, where it actually comes from:

– Various types of documents prepared when there is a new product launch

– Data from social networks like Twitter, Facebook and other web interfaces and the email also

– Transaction data from systems like ERP, SAP, and OLTP

– Another source from where a huge amount of data is getting created is mobile phones.

Every data being created from these sources is providing rise to crucial the big data challenges. To meet the requirement of big data sets for being stored and analyzed, we must have a device, which can match with the type of development we are getting in terms of data creation, and can analyze and finally generate an insight about the data.

Rise of Hadoop:

The best thing is that in spite of many big data here, we also have a technology with us like Hadoop, which gives with a system like MapReduce and tools, such as Cassandra, HBase, NoSQL, Pig, and Hive. By utilizing them, we can procedure the big amount of data by engaging distributed computing idea and we can procedure it also. However then, the challenge which we have is how to actually store, understand and analyze the data.

Since the starting of the era of development of big data, the data is becoming huge and the issue of processing that big amount of data has been rising. At first, the companies getting a big amount of data tried for solving this issue utilizing the existing RDBMS type of solutions, yet were not capable of scaling out anything in terms of the speed. The requirement of processing big data and get an understanding out of it drove to the requirement for a technology like Hadoop. The boundless utilization of Hadoop has presently lessened the Big Data challenges very much.

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