What storage systems can RDDs utilize besides memory?

RDDs (Resilient Distributed Datasets) in Apache Spark can utilize various storage systems beyond just memory. This flexibility is one of the key features that allows RDDs to efficiently handle large datasets and support diverse data sources.

HDFS (Hadoop Distributed File System) is a highly distributed storage system designed to provide high-throughput access to application data, making it an excellent choice for storing large datasets in a distributed environment. Additionally, HBase, which is built on top of HDFS, provides NoSQL database capabilities, enabling random access to large amounts of structured data.

By leveraging these storage systems, RDDs can efficiently read from and write data back to durable storage, allowing for fault tolerance and scalability. This can be particularly beneficial in scenarios where data needs to be processed across multiple nodes in a cluster while ensuring data persistence.

Other choices like cloud storage solutions or database systems could also store data that might eventually be read into RDDs, but the question specifically highlights HDFS and HBase, which are designed to work seamlessly within the Hadoop ecosystem and integrate closely with Spark's architecture. This makes the inclusion of HDFS and HBase in the correct answer particularly relevant for understanding how RDDs can operate in various storage contexts.