In what situations should Hadoop, HBase, Hive, and Pig be employed?

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Hadoop is designed to handle large-scale distributed data processing, making it ideal for situations where large amounts of data need to be processed and analyzed quickly.

HBase is a NoSQL database that is built on top of Hadoop and is optimized for real-time read/write access to large datasets. It's ideal for use cases where random access to large amounts of data is required, such as storing time-series data or event data.

Hive is a data warehousing tool that allows users to easily query and analyze data stored in Hadoop. It's best suited for situations in which users are familiar with SQL and need to regularly query large datasets.

Pig is a high-level data flow language that allows users to create complex data processing pipelines in Hadoop. It's best suited for situations where complex data transformations are required, such as when dealing with unstructured data or parsing large log files.