Difference between Data Mining and Big Data?

Data Mining

Data mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through a large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. It is the analysis of observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.

Data mining can include the use of several types of software packages including analytics tools. It can be automated, or it can be largely labor-intensive, where individual workers send specific queries for information to an archive or database.

Generally, data mining defines operations that contain relatively sophisticated search operations that return focused and definite results. For instance, a data mining tool can view through dozens of years of accounting data to find a definite column of expenses or accounts receivable for a specific operating year.

Big Data

Big Data refers to the vast amount that can be structured, semi-structured, and unstructured sets of data ranging in terms of tera-bytes. It is complex to process a large amount of data on an individual system that's why the RAM of this computer saves the interim computation during the processing and analyzing. When we try to process such a huge amount of data, it takes much time to do these processing steps on a single system. Also, our computer system doesn't work correctly due to overload.

Big data sets are those that outgrow the simple type of database and data handling structure that were used in previous times when big data was more highly-priced and less feasible. For instance, sets of data that are too high to be simply handled in a Microsoft Excel spreadsheet can be defined as big data sets.

Let us see the comparison between Data Mining and Big Data.