Which architecture is commonly associated with Big Data technologies?

The architecture commonly associated with Big Data technologies is distributed architecture. This approach is pivotal for handling the vast volumes of data generated in today's digital world. Distributed architecture allows for the processing of large datasets across multiple machines or nodes, which offers scalability and flexibility.

In a distributed system, data can be stored and processed on various servers, enabling parallel processing and fault tolerance. This is particularly important when dealing with Big Data, as it often exceeds the capacity of a single machine. Technologies like Hadoop and Spark leverage distributed architecture to manage data across a cluster of computers, allowing for efficient computation and storage solutions.

The benefits of a distributed architecture align well with the core needs of Big Data, including the ability to process data in real-time, accommodate growth in data volume, and ensure reliability through redundancy. This architecture's design is integral to achieving the performance and scalability required in modern data environments.