What is the process of handling data in Hadoop?

The process of data processing in Hadoop can be divided into the following steps:

1. Data preparation: Load the raw data into the Hadoop Distributed File System (HDFS) and clean, transform, and preprocess it as needed.
2. Data partitioning: dividing data into appropriate sizes to allow for parallel processing within a Hadoop cluster. The partitions can be files, lines, blocks, etc.
3. Data storage and computation: Utilizing the MapReduce programming model in Hadoop to distribute computation tasks to multiple nodes in the cluster for parallel processing. Data is stored in HDFS and computed using MapReduce tasks.
4. Data transmission and processing: During the Map phase, data is partitioned and sorted according to specified key-value pairs, and the results are then passed on to the Reduce phase. In the Reduce phase, the data is merged, summarized, and calculated.
5. Aggregating and outputting data: combining the results of the Reduce phase and storing the final outcome in HDFS, or sending it to an external storage system or application.
6. Data cleaning and optimization: Perform data cleaning and optimization based on requirements, including deleting unnecessary intermediate results, compressing data, and adjusting task parameters.
7. Data analysis and visualization: analyzing and visualizing data stored in HDFS using tools and technologies from the Hadoop ecosystem, such as Hive, Pig, and Spark.

In general, the process of handling data in Hadoop involves loading data into HDFS, performing parallel computation and processing through MapReduce tasks, and finally storing or outputting the results.