Data Mining vs Big Data

It's a common myth that big data and data mining seem to be the same thing, they're not! The similarities between such two terms include the use of massive data, the processing of information and the presentation of data being used by a company. Having said all that, it should be comprehended that Big Data Analytics and Data Mining are used for 2 distinct activities. Let's walk through these two terms big data and data mining in depth.

What is big data?

Big data serves as a huge or massive data, knowledge or statistical information obtained by big enterprises and endeavors. A lot of data and hardware storage has been generated and equipped as it is easier to visualize big data individually. It's being used to explore trends and patterns and to make important decisions associated with human actions and communication innovations.

Why big data?

Companies that rely on big data to enable them to make business decisions. Big data analysis empowers data scientists, analytics professionals and other experts to evaluate high volumes of data records. Big data may also be used to interpret data which may not have been realized by standard business classes. This contains the following such as reports on social media activity and social media network activity, information from sensors linked to the IOT, consumer emails and questionnaire replies, web application logs and Clickstream information, etc.

Many of the factors in today's world are motivated by the profit margins they offer in terms of financial benefits, they assist to include useful insights for effective management decisions, and individuals could also be used to research lots of other things which might help mankind.

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Big data processing:

In order to process the big data, we take advantage of the hadoop technology. Big data consists of 5 v’s such as velocity, volume, variety, value and veracity.

Hadoop is a free and open source framework that operates in a distributed environment deliberately. The hadoop distributed system follows the below mentioned modules such as:

  • Hadoop common contains basic utilities and dictionaries that are required by all the hadoop modules.
  • Hadoop distributed file system is a  system that stores the data on a commodity system for the users.
  • Hadoop yarn:hadoop yarn is an effective resource management system where it allocates the resources to the user applications accurately.
  • Hadoop mapreduce:It is one of the programming frameworks for processing the bulk volumes of data sets at an instant.

What is data mining?

Data Mining is a method used to retrieve essential knowledge and information from a massive information set/library. It originates from perspective by thoughtfully retrieving, evaluating, and handling vast information to track patterns and connections which may be of value to the firm. It is similar to gold mining, in which gold is obtained from rocks and sands.

Why is data mining important?

Data Mining is essential for a variety of purposes, the much more essential and helpful of it is to comprehend what's really appropriate and to make better use of the things in order to evaluate the truths as the fresh information emerges, turning subsidiaries into different applications in the places such as healthcare, financial market analysis, etc.

Here are some key data mining parameters includes:

  • Association: Assessing the trends at which activities are linked
  • Path analysis: Kept an view on the activities that contributes to another 
  • Clustering: Clustering is also where communities of actual statistics that were previously unseen are found and recorded. 
  • Forecasting : Use patterns in data to create predictive models sound and reliable.
  • Classification: This is going to keep an eye out for completely new and different trends. This could cause an organizational change of the data. And that's a common one.

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Data mining Processing:

There are several steps involved in data mining processing. They are:

  • Data Integration: Here the data is collected from different sources and integrated into one platform.
  • Data selection: Here we will collect the data that is left in the data integration section.
  • Data cleaning: data collected needs to perform some basic operations to reduce the errors in the data.
  • Data Transformation: Even after data cleaning is prepared it might be not ready so we need to transform them into structures for the data mining.
  • Data Mining: After the mining is completed, data mining methods are implemented to extract the useful data.
  • Pattern evaluation: In this step we prepare the visualization, transforming from the patterns developed in the above section.
  • Decision: here the useful data is taken to generate the meaningful decisions without impacting the business outcomes.

Comparison between data mining and big data:

In this section, we will explore the key difference or comparison between the two analytical processes such as data mining and big data in detail.

  • Data mining serves as a subset of big data whereas big data serves as a superset of data mining.
  • Knowledge discovery has become a vital part in data mining, moreover it acts as the close observation of the data.Where as big data is a method of extracting useful and valuable data form the complex data sets.It acts a s an complete view of the datasets.
  • Data mining can be done manually and automated also. Whereas in big data data analytics is automated because of the bulk volumes of data.
  • Data mining entirely focuses or concentrates only on form of the data i.e structured data, whereas big data concentrates on both the structured, unstructured and semi structured data.
  • Data mining refers to what about the data whereas big data represents why about the data.
  • Data mining is mainly used to make strategic an doptimizined business decisions whereas big data is used for predictive analytics and dashboard reporting techniques.
    It performs statistical analysis and helps the business concerns of small organizations whereas big data is based on data analysis and relies for the large organizations.

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Conclusion:

Yes, guys, the above mentioned data is good enough to know about the key differences between the data mining and big data techniques and how they are used in the real time analysis of complex data sets in an organization.Moreover if you find any relevant data pertaining to these concepts please do comments we will definitely consider and add it to our piece.

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Saritha Reddy
Saritha Reddy
Research Analyst
A technical lead content writer in HKR Trainings with an expertise in delivering content on the market demanding technologies like Networking, Storage & Virtualization,Cyber Security & SIEM Tools, Server Administration, Operating System & Administration, IAM Tools, Cloud Computing, etc. She does a great job in creating wonderful content for the users and always keeps updated with the latest trends in the market. To know more information connect her on Linkedin, Twitter, and Facebook.