Performance :

• Apache Spark is up to 100 times faster than MapReduce. Spark can cache data in-memory, ensures low latency, MANAGES data using controlled PARTITIONS and uses maximum memory of the cluster to process data.
• Unlike Spark, MapReduce can’t use the memory of the Hadoop cluster to the maximum and It uses Disk memory to STORE intermediate data in each stage. So, it spends more time to read and write intermediate data while processing data.

Data Processing :

• As Spark has many INBUILT libraries, it can process the data using SQL queries, real-time streaming, batch processing, and MACHINE learning data.
• MapReduce supports only batch data processing.

Interactivity:

• Apache Spark has interactive mode via spark shell.
• MapReduce doesn’t have an interactive mode.

Difficulty:

• Apache Spark is easy to program because of high-level APIs.
• MapReduce is difficult to program because it needs abstraction.

Independent of Hadoop :

• Apache Spark is independent of Hadoop, can run without Hadoop.
• MapReduce can be run only on Hadoop System.