What is the difference between Apache spark and Apache Hadoop MapReduce?

Comparing Hadoop and Spark The primary difference between Spark and MapReduce is that Spark processes and retains data in memory for subsequent steps, whereas MapReduce processes data on disk. As a result, for smaller workloads, Spark’s data processing speeds are up to 100x faster than MapReduce.

What are the disadvantages of using Apache spark over Hadoop MapReduce?

What are the limitations of Apache Spark

• No File Management system. Spark has no file management system of its own.
• No Support for Real-Time Processing. Spark does not support complete Real-time Processing.
• Small File Issue.
• Cost-Effective.
• Window Criteria.
• Latency.
• Less number of Algorithms.
• Iterative Processing.

What are the limitations of Hadoop and how Spark overcomes these limitations?

No Caching In Hadoop, MapReduce cannot cache the intermediate data in memory for a further requirement which diminishes the performance of Hadoop. Spark and Flink can overcome this limitation of Hadoop, as Spark and Flink cache data in memory for further iterations which enhance the overall performance.

Why Apache Spark is faster than MapReduce?

In-memory processing makes Spark faster than Hadoop MapReduce – up to 100 times for data in RAM and up to 10 times for data in storage. Iterative processing. Spark’s Resilient Distributed Datasets (RDDs) enable multiple map operations in memory, while Hadoop MapReduce has to write interim results to a disk.

What is limitation of Hadoop?

Limitations of Hadoop

• a. Issues with Small Files. The main problem with Hadoop is that it is not suitable for small data.
• b. Slow Processing Speed.
• c. Support for Batch Processing only.
• d. No Real-time Processing.
• e. Iterative Processing.
• f. Latency.
• g. No Ease of Use.
• h. Security Issue.