You can use three important metrics to distinguish between Hadoop and Spark and find out which is the best:
· Speed: Spark is essentially a general-purpose cluster computing tool and when compared to Hadoop, it executes applications 100 times faster in memory and 10 times faster on disks. This is possible because Spark reduces the number of read/write cycles on the disk and stores the data in memory.
· Ease of use: Spark has the capability to perform batch processing, streaming, and interactive data analysis in the same cluster, which makes Apache Spark a complete data analysis engine! On the other hand, MapReduce only provides a batch-processing engine and we need to use a different engine for each task, thus, managing more than one component is very difficult.
· Real-time Analysis: Spark can easily handle real-time data and can process real-time streams (live). However, MapReduce fails here because it is only used to execute batch processing for large amounts of data, so real-time data processing cannot be performed.
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Here is a video tutorial which you can watch to learn more about spark:-
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