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Purpose: Distributed processing of large data sets across clusters of computers. Key Characteristics: Scalable, fault-tolerant and open-source framework.

Purpose: A distributed, versioned, column-oriented store modeled after Google's Bigtable, providing big table-like capabilities for Hadoop. Key Characteristics: Linear and modular scalability.

Purpose: A NoSQL database designed for ease of development and scalability. Key Characteristics: Document-oriented, high performance, and high availability.

Purpose: A distributed streaming platform that is used to build real-time data pipelines and streaming apps. Key Characteristics: Durability, high-throughput, scalability.

Purpose: A stream processing framework that can also handle batch processing. Key Characteristics: True streaming model, fault tolerance, high throughput.

Purpose: Data warehouse software to facilitate easy data summarization, querying, and analysis of large datasets stored in Hadoop files. Key Characteristics: SQL-like language called HiveQL, plug-in to MapReduce framework.

Purpose: General-purpose distributed data processing engine that extends the MapReduce model to efficiently process big data. Key Characteristics: Speed, ease of use, and sophisticated analytics.

Purpose: A high performance, distributed SQL query engine for big data. Key Characteristics: Federated queries, in-memory processing, interactive analysis.

Purpose: A platform for analyzing large datasets that consists of a high-level language for expressing data analysis programs. Key Characteristics: Abstraction over MapReduce, extensibility through user-defined functions, optimization opportunities.