a) Spark needs higher RAM whereas MapReduce needs larger disk space in terms of big data processing. On the cloud, Spark will definitely outperform MapReduce.
b) Hadoop has been around since 2005, there is still a shortage of MapReduce experts out there on the market. What does this mean for Spark, which has only been around since 2010? Maybe it has a faster learning curve, but it still lacks way more skilled ninjas out there compared to Hadoop MR.
c) Spark’s compatibility to data types and data sources is the same as Hadoop MapReduce.
d) Spark and Hadoop MapReduce both have good failure tolerance, but Hadoop MapReduce is slightly more tolerant.
e) Spark security is still in its infancy; Hadoop MapReduce has more security features and projects.