What is an example of a distributed computing framework besides Hadoop?

Spark is a prime example of a distributed computing framework that is designed to process large volumes of data efficiently. Unlike Hadoop, which relies on a batch processing model, Spark processes data in-memory, which significantly speeds up data processing tasks. This allows Spark to leverage the resources of a cluster by distributing data and computations across multiple nodes, enabling parallel processing. As a result, Spark can handle various types of workloads, including batch processing, stream processing, and interactive queries, making it a versatile tool in big data environments.

In contrast, while tools like Kafka are integral for real-time data streaming and message brokering, they do not serve as distributed computing frameworks themselves. SQL Server is a relational database management system that primarily focuses on data storage and retrieval rather than distributed computing. Pandas is a data manipulation and analysis library in Python, best suited for processing data on a single machine rather than in a distributed manner. Therefore, Spark stands out as a clear example of a distributed computing framework beyond Hadoop.