What is the difference between Spark and Hadoop?

The main distinctions between Spark and Hadoop lie in their nature as two separate open-source big data processing frameworks.

1. Data processing models: Hadoop utilizes a batch processing model, where data is partitioned into small tasks for processing using MapReduce. On the other hand, Spark employs an iterative computing model that allows for caching data in memory and efficient data processing through RDDs (Resilient Distributed Datasets).
2. Memory management: Hadoop stores data on disk, while Spark utilizes memory for data caching and processing, making it faster in terms of processing speed.
3. Due to the fact that Spark uses memory for data processing, the efficiency of handling tasks such as iterative computations and interactive queries that require multiple data reads is higher with Spark.
4. Data processing capabilities: Spark offers a wider range of data processing abilities, such as batch processing, interactive queries, real-time streaming processing, and machine learning, while Hadoop is primarily used for batch processing.
5. Both Hadoop and Spark have their own extensive ecosystems, with Hadoop consisting of components like HDFS, YARN, and MapReduce, and Spark having Spark Core, Spark SQL, Spark Streaming, and MLlib.

In conclusion, Spark and Hadoop have significant differences in their data processing models, memory management, processing efficiency, data processing capabilities, and ecosystem support. The specific choice of which framework to use depends on the actual needs and scenarios.