Data cleaning and standardization: Firstly, clean and standardize the data from different sources to ensure consistent data formats and eliminate duplicate and erroneous data.
Data Integration: By integrating the cleaned data into the Hadoop platform, one can use the Sqoop tool to import data from relational databases into Hadoop, or use the Flume tool to collect real-time data flow into Hadoop.
Data storage: Save data from various sources into the Hadoop distributed file system HDFS for further analysis and processing.
Data processing: Utilizing tools within the Hadoop ecosystem such as MapReduce, Hive, Spark, etc., for data processing and analysis allows for operations such as data aggregation, statistics, and mining.
Data visualization: Using tools like Tableau and PowerBI to visually display processed data, helping users better understand the results of data analysis in a more intuitive way.
Data security: It is important to ensure the security of data during the process of data integration and analysis, measures such as permission control and encryption can be used to protect the confidentiality and integrity of the data.
