Apache Spark is often compared to Hadoop as it is also an open-source framework for big data processing. In fact, Spark was initially built to improve the processing performance and extend the types of computations possible with Hadoop MapReduce. Spark uses in-memory processing, which means it is vastly faster than the read/write capabilities of MapReduce.
While Hadoop is best for batch processing of huge volumes of data, Spark supports both batch and real-time data processing and is ideal for streaming data and graph computations. Both Hadoop and Spark have machine learning libraries, but again, because of the in-memory processing, Spark’s machine learning is much faster.
