Which open-source technology provides distributed storage and processing of big data, allowing scalability and support for various data formats?

Apache Hadoop stands out as a foundational open-source framework designed for the distributed storage and processing of large data sets, making it highly scalable. It uses the Hadoop Distributed File System (HDFS) for storing vast amounts of data across clusters of computers, ensuring that data can be stored in a fault-tolerant manner. This scalability allows organizations to expand their storage capacity by simply adding more machines to the cluster, without needing to overhaul their existing systems.

In addition to HDFS, Hadoop includes a processing model called MapReduce, which efficiently processes large volumes of data in parallel across the distributed architecture. Furthermore, Hadoop's versatility allows it to support various data formats, whether structured or unstructured, enabling it to accommodate a wide range of applications and use cases.

Other options, while relevant in their own areas, do not provide the full suite of capabilities that Hadoop offers in the realm of distributed storage and processing. For instance, Apache Hive is built on top of Hadoop and is primarily focused on data warehousing and SQL-like querying capabilities rather than core data processing. Apache Spark, while a powerful data processing engine that can work dynamically with memory, is often used as an alternative to MapReduce but does not provide its own data storage system. NoSQL databases, though