What is Apache Hive primarily used for?

Apache Hive is primarily designed for data warehousing and enables SQL-like querying of large datasets stored in Hadoop. It acts as an abstraction layer on top of the Hadoop ecosystem, allowing users to interact with large amounts of data using HiveQL, which is similar to SQL. This capability is particularly valuable for organizations dealing with massive volumes of structured or semi-structured data, as it simplifies data analysis and reporting tasks.

Through its architecture, Hive facilitates reading, writing, and managing vast data sets, making it highly suitable for batch processing. Its ability to convert SQL-like queries into MapReduce jobs allows users to leverage the distributed computing power of Hadoop seamlessly, which is essential for handling Big Data applications. The focus on data warehousing underscores its purpose as a platform for aggregating, summarizing, and querying large datasets efficiently, which aligns perfectly with the needs of data analysts and engineers in a Big Data context.