Data scale: big data usually refers to massive amounts of data that cannot be processed with conventional software tools within a certain period of time. Small data, on the other hand, refers to data that is relatively small in size and can be processed using conventional software tools.
Data sources: Big data can come from a variety of sources, including traditional data sources (e.g., databases, enterprise information systems, etc.) and non-traditional data sources (e.g., social media, satellite imagery, Internet logs, etc.). Small data usually originates from traditional data sources.
Data processing: big data requires the use of special data processing techniques (e.g., distributed computing, data mining, etc.) for processing. Small data can be processed using conventional software tools.
Data analysis methods: big data analysis usually requires the use of methods such as machine learning, artificial intelligence and data mining in order to extract valuable information from massive data. Small data, on the other hand, can be analyzed using conventional statistical analysis methods.