Big Data: A Tool for Dealing with Massive Data

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     Big Data is a field of Science that is a combination of structured, semi-structured and unstructured volumes of large data collected by organizations. It is a term applied to the way of analyzing huge amounts of data which either may be too large or complex to deal with, systematically extracting information from it and then successfully drawing insights based on it. In Big Data, advanced analytical techniques are used for dealing with large, diverse data sets obtained from different sources and available in different sizes from terabytes to zettabytes. The source of Big Data is generally the sensors, devices, video and audio clips, log files, networks, web applications, transactional applications and social media. Much of the Big Data is generated from all these sources at a very large scale in real-time.

What exactly is Big Data?

Big Data as the name suggests is a term employed for such datasets whose size or type is completely beyond the ability of the traditional and relational databases to manage, capture, analyze and process the raw data with more latency. Moreover, the datasets are not always well structured. They may be available in the form of structured, semi-structured and unstructured data form. Big Data is so massive because it possesses a variety of key traits which are volume, veracity, value, velocity, variety, exhaustivity, resolution, indexicality, relationality, extensionality and scalability. Big Data isn’t limited, it is still growing in size in real-time. 

Types of Big Data

In earlier times, the data was small and so the Relational Database Management Systems and desktop software statistical packages used to visualize the data and then analyze and process that data. However, with the technological changes taking place, the data has increased tremendously in volume and has even become more complex. This is due to the Big Data splitting itself into various types. The types of Big Data are:
  • Structured:
    Any data that is accessed, stored or processed in fixed format form is termed a ‘Structured’ data form. Over the years, a variety of tools, techniques and algorithms have been developed for working with such kind of data and deriving results from it. In such databases, the format is well known in advance.
  • Unstructured:
    This type of data usually contains data with unknown form or unknown structure. Compared to the Structured database, dealing with Unstructured database is quite challenging. An example of an Unstructured database is a heterogeneous database containing a combination of text files, audio, images, videos, etc.
  • Semi-structured:
    Semi-structured data is a combination of structured and unstructured databases. This data does look structured but is seemingly difficult to handle and possesses the same difficulty to handle as in the case of unstructured databases. A simple example of a semi-structured datatype is data represented in the XML file format.

Characteristics of Big Data

Big Data possess a variety of key traits out of which the five V’s are the key traits which have made Big Data a huge business. These characteristics are: 
  • Volume
    As the name of Big Data suggests, it deals with a huge amount of data. Big Data is related to the enormous size of data and this size does play a very crucial role in determining the insights from that data. Any data can be classified as Big Data based on the volume of the data. Any data above 1Tera Byte can be considered as a Big Data database.
  • Variety
    The variety of data is also important in any database classification. Variety generally refers to the heterogeneous sources and also the nature of data may it be structured, unstructured or semi-structured. During the earlier times, data was mainly in the form of spreadsheets and databases. But nowadays the form of data has taken a variety of looks from emails, photos, texts, videos, audio, emojis, PDFs, monitoring devices, etc. Now all such data types are taken into consideration for analysis applications. At times, the Unstructured data possess issues with storage, mining and analyzing the data.
  • Velocity
    Velocity is a term that in this case refers to the speed of the Big Data generation. The speed at which the Big Data is generated and processed does matter to meet the demands and to determine the real potential of the data. The flow of data is enormous and continuous from sources such as business processes, application logs, sensors, networks, mobile devices, social media sites, etc.
  • Variability
    This term is of great importance when dealing with Big Data as it refers to the inconsistency that can be shown by the data and thus can disrupt the process of handling and managing the data effectively. So, any data that is to be analyzed need to be consistent for faster, smoother and appropriate outcomes.
  • Value
    Any data that is collected is classified based on the value that it has. For instance, any data with greater value is promoted over the other with slightly less value. Some data at times may have various anomalies or can be not so important for deriving the results and some other data may be collected with more important information and with higher value so they are considered likewise and given preference as per that.
Working on Big Data
Big data technologies give the user new insights thus opening new opportunities and business-related models. So, working with Big Data is a real thing to know. The Big Data work involves mainly the following steps: 
  1. Integrating the raw data
    For the working of Big Data processing, raw data needs to be collected from various sources and applications. Traditionally the extraction of data was done using the integration mechanism which followed the extraction, transformation and loading (ETL). But now they are not compatible for handling the Big Data related datasets. So now to handle data of the terabyte, zettabyte, or petabyte scale the integration mechanism has changed. It involves bringing in the raw data processing it, and making sure it’s formatted and also available in the form that the business analysts can work with.
  2. Managing the Data
    As the data is big, it requires storage enough to fit that big data. So, the storage is either in the cloud, premises or a combination of it. The data is stored in any form and only the required data is brought which is necessary for the processing part also the process engines are brought which are required for those datasets on an on-demand basis. Nowadays, the Cloud is gradually gaining popularity because it supports the user’s present computer requirements and also enables the user to ship resources as per requirement.
  3. Analyzing the data
    The analyzing and acting upon the data is made possible with the Big Data technologies. It even helps in getting clarity of the varied datasets and for exploring the data to make further discoveries. Also, the data can be shared and based on that a Machine Learning or Artificial Intelligence model is built and the data can be put to work.



Advantages of Big Data

With the Big Data processing coming up, huge loads of data can now be conveniently handled. The ability to process a huge amount of data in DBMS brings in a variety of benefits such as: 
  • Utilization of outside intelligence by businesses while making important decisions.
    For example, businesses can now have access to social data from various search engines like Google, Twitter, Facebook, Instagram, etc. and thus from the data collected can very easily finetune their businesses and increase their profit margins.
  • Providing improved customer service.
    For example, Earlier the feedback system was not that impressive. But with the use of Big Data processing customer feedback can easily be noted and taken into consideration. This in a way helps companies improve customer service and give their customers more satisfactory services.
  • Early identification of any risk to the product or service.
    For example, businesses now before launching their product or service in the market can easily ensure that their product or service is safe to use or if it has any risk in the market. So, any sort of identification is now possible with all the processes coming in.
  • Better operational efficiency
    Nowadays, Big Data processing technologies can be used for the creation of a staging area or landing point for any new raw data. So, this gives the option of identifying what data should be moved to the data warehouse. Also, with the addition of Big Data technologies and the data warehouse organizations can now offload infrequently accessed and used data thus reducing the data load.
Big Data is really a big thing to talk about. To get more insights into Big Data, a thorough understanding of the concept is necessary. To understand Big Data and related technologies, log into the DockLearn website and explore more about it.

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