Big Data: It is huge, large or voluminous data, information or the relevant statistics acquired by the large organizations and ventures. Many software and data storage created and prepared as it is difficult to compute the big data manually.
It is used to discover patterns and trends and make decisions related to human behavior and interaction technology.
Data Mining: Data Mining is a technique to extract important and vital information and knowledge from a huge set/libraries of data. It derives insight by carefully extracting, reviewing, and processing the huge data to find out pattern and co-relations which can be important for the business. It is analogous to the gold mining where golds are extracted from rocks and sands.
Below is a table of differences between Big Data and Data Mining:
|Big Data||Data Mining|
|It is one of the method in the pipeline of Big Data.||Big Data is a technique to collect, maintain and process the huge information. It explains the data relationship.|
|Data mining is a part of Knowledge Discovery of the Data. It is close view of the data.||It is about extracting the vital and valuable information from huge amount of the data.It is a technique of tracking and discovering of trends of complex data sets. It is a large or overall view of the data.|
|The goal is same as Big Data as it is one of the tool of Big Data.||The goal is to make data more vital and usable i.e. by extracting only important information from the huge data within existing traditional aspects.|
|It is manual as well as automated in nature.||It is only automated as computing huge data is difficult.|
|It only focuses on only one form of data. i.e. structured.||It focuses and works with all form of data i.e. structured, unstructured or semi-structured.|
|It is used to create certain business insights. Data mining is a manager of the mine.||It is mainly used for business purposes and customer satisfaction. Big Data is a mine.|
|It is a sub set of Big Data. i.e. one of the tools.||It is a super set of Data Mining.|
|It is a tool to dig up the vital information from the large data. Data can be large as well as small.||It is more involved with the processes of handling voluminous data. Data can only be large.|
- Difference between Data Warehousing and Data Mining
- Difference Between Data Mining and Data Visualization
- Difference Between Data Science and Data Mining
- Difference Between Data Mining and Data Analysis
- Difference Between Data Mining and Text Mining
- Difference Between Data Mining and Web Mining
- Difference between Data Mining and OLAP
- Difference Between Data Mining and Statistics
- Difference between Spatial and Temporal Data Mining
- Difference Between Data mining and Machine learning
- Difference between Business Intelligence and Data Mining
- Data Mining: Data Attributes and Quality
- Data Mining: Data Warehouse Process
- Types of Sources of Data in Data Mining
- Data Normalization in Data Mining
- Data Integration in Data Mining
- Data Reduction in Data Mining
- Data Preprocessing in Data Mining
- Data Transformation in Data Mining
- Difference between Data Scientist, Data Engineer, Data Analyst
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