The big data processing process is as follows:

1. Data collection: collect data from various data sources, including sensor data, log files, social media data, transaction records, and so on. Data collection can be done in various ways, such as API interfaces, crawlers, sensor devices and so on.

2. Data storage: store the collected data in appropriate storage media, such as relational databases, distributed file systems, data warehouses, or cloud storage. Choosing the appropriate storage method depends on the nature, scale and usage requirements of the data.

3, data cleaning and preprocessing: cleaning, filtering and preprocessing of raw data to remove noise, deal with missing values, resolve data inconsistencies and other issues to ensure data quality and consistency.

4, data conversion and integration: Integrate and convert data from different sources to conform to specific data model and format requirements. This may involve data structuring, normalization, merging and other operations.

5. Data analysis: the application of statistical analysis, machine learning, data mining, and other techniques to the cleaned and transformed data in order to discover patterns, trends, and correlations in the data and extract useful information and knowledge.

6. Data Visualization: Display the analysis results in a visual way, such as charts, graphs, maps, etc., to make the data easier to understand and interpret, and help users make decisions and insights.

Characteristics of Big Data

1, large volume: one of the most significant features of big data is the huge volume of data, far beyond the scope of traditional data processing capabilities. They may contain billions, tens of billions or even more of records and observations.

2. Diversity: Big Data covers a wide range of data types and formats, including structured data (e.g., tabular data in relational databases), semi-structured data (e.g., XML and JSON files), and unstructured data (e.g., text, images, audio, and video).

3. Timeliness: Big data is usually generated in real time or quickly, and needs to be processed and analyzed in a timely manner in order to derive value from the data.

4, high speed: Big data is generated very quickly, requiring real-time or near real-time processing and analysis of the data. Data may be generated and updated at a rate per second or even faster.