Hive is a data warehousing and SQL-like query language for Hadoop that allows users to process and analyze large datasets using SQL-like queries. Hive internally uses MapReduce to execute these queries on Hadoop clusters. Here’s how Hive and MapReduce work together:

1. Hive Query Compilation: When you write a HiveQL query (similar to SQL), Hive compiles it into a series of MapReduce jobs. HiveQL is a high-level language that abstracts the complexities of writing low-level MapReduce code.

2. Query Optimization: Hive performs query optimization to generate efficient execution plans for the MapReduce jobs it generates. This includes optimizing the query plan, selecting the appropriate join strategies, and determining how data should be distributed across nodes.

3. Translation to MapReduce: Hive translates the optimized query plan into a series of MapReduce jobs. These jobs include a series of mappers and reducers that will process the data.

4. Data Processing: The MapReduce jobs generated by Hive are executed on the Hadoop cluster. Mappers process the input data and emit intermediate key-value pairs. Reducers then process these intermediate key-value pairs to produce the final result.

5. Hive Metastore: Hive stores metadata about tables, schemas, and partitions in a metadata repository called the Hive Metastore. It keeps track of where the data is located in HDFS and how it should be processed using MapReduce.

6. Data Storage: The data managed by Hive is typically stored in Hadoop’s HDFS (Hadoop Distributed File System) or other compatible file systems. Hive doesn’t manage the data storage itself but provides a logical layer on top of the raw data.

7. Data Serialization and Deserialization: Hive uses SerDes (Serializer/Deserializer) to read and write data in various formats. These SerDes are pluggable and allow Hive to work with different data formats, such as JSON, Parquet, ORC, and more.

8. Execution Engine: The actual execution of the MapReduce jobs generated by Hive is handled by the Hadoop MapReduce framework. Hive leverages the MapReduce cluster’s resources to execute these jobs efficiently.

9. Intermediate Data: During the MapReduce process, intermediate data is shuffled and sorted as it moves between the mappers and reducers. This intermediate data handling is managed by Hadoop’s MapReduce framework.

10. Result Presentation: The final result of the Hive query is presented to the user in tabular format, similar to the result of a SQL query. Users can retrieve this result using various interfaces, including the Hive CLI, JDBC/ODBC connectors, or web-based interfaces.