Implementing Big Data Analytics? Tools That Make It Possible

2 mins read

With the bid to stay ahead in the competitive market, becoming stronger by the day, being technologically updated in every way has become quite prominent. And in this context, managing huge volumes of data has also become quite important. The recent buzz in the technological field that has successfully captured the attention of many is the big data. So, what is the big data all about?

What Is Big Data Analytics?

Before anything is said about the big data analytics part, it is important to understand what big data is all about. To put it in very simple terms, big data can be said to be a cluster of data- both structured and unstructured. The amount of data is so high in volume that it becomes impossible to handle the entire thing using conventional software and database systems.

Big data analytics is a procedure that involves the examination of copious amounts of data to reveal patterns and correlated information, that may have been overlooked by “Business Intelligence programs”, so as to run business organizations and the like in a more efficient manner. Big data analytics is frequently used in relation to unstructured data, but there are also organizations that use the same for managing structured data as well.

There might be other data sources as well that big data analytics may incorporate such as, “web server logs”, “social media activity reports”, “mobile phone call detail record” and the like.

Tools For Big Data Analytics Implementation

Now the question that needs addressing is what are the possible tools for big data analytics? Software tools, usually put to use in “advanced analytics discipline”, like “data mining” and “predictive analytics” are put to use for the purpose of big data analytics. However, it should be noted that such software may not be compatible with the requirements of the unstructured data. Hence, there has arrived a completely new set of technological tools that are being used for the purpose of big data analytics.

  • Hadoop– A part of the Apache project, Hadoop is a “Java-based programming framework” that makes applications operating with thousands of terabytes as something possible. Its dispersed system of files aids in facilitates rapid rates of data transfer between the nodes, thereby, ensuring that a node failure does not interrupt the operation. This means that the risk of losing data due to system failure becomes lower by a considerable extent. Map Reduce from Google forms the inspiration for the designing of this tool, wherein, data used to be broken down into a number of smaller units, which could then be operated on any node in the group. The current system of Hadoop is an assimilation of major tools- “the Hadoop kernel”, “Hadoop Distributed File System”, “Map Reduce”.
  • Map Reduce– Developed by Google, primarily for the purpose of indexing web pages, Map Reduce is a software scheme, using which developers can write programs that have the capability of processing quite a huge volume of unstructured data across stand-alone computers. The entire framework is basically divided into two parts- “Map” and “Reduce”. The Map distributes work among the nodes in the dispersed cluster and Reduce collates and assimilates the results to produce a “single value”. Tasks such as “data mining”, “financial analysis”, “logfile analysis” and “scientific simulations” are some of the important areas where Map-Reduce comes in handy.

NoSQL– Not Only SQL is a big data analytics tool that is especially handy when enterprises need to access and analyze huge volumes of unstructured data or data that is stored using remote servers. The organization of data using the NoSQL tool involves objects and tuples, instead of tables.

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