Hadoop Big Data Processing Guide

To process large-scale data using Hadoop, the first step is to install and configure a Hadoop cluster. Then, you can use the following steps to process large-scale data with Hadoop:

1. Write MapReduce program: Develop a MapReduce program in Java to deal with data. MapReduce is a core component of Hadoop, utilized for parallel processing of large-scale datasets.
2. Store data in the distributed file system of Hadoop (HDFS): Store the data to be processed in the HDFS of the Hadoop cluster so that MapReduce programs can access and process the data.
3. Submit a MapReduce job: Submit the MapReduce program written to the Hadoop cluster, and allocate resources to execute the job through Hadoop’s resource manager.
4. Monitor job execution: Monitor the execution status and progress of MapReduce jobs, which can be viewed through Hadoop’s web interface or command line tools.
5. Obtain processing results: Once the MapReduce job is completed, the processed data can be retrieved from the Hadoop cluster for further analysis and processing.

By following the steps above, one can utilize Hadoop to process massive amounts of data and achieve parallel processing and analysis. Additionally, Hadoop also offers other tools and components like Hive, Pig, and Spark that further simplify and optimize the process of handling large-scale data.