Key Differences between Hadoop MapReduce and Spark

When it comes to Big Data processing, Hadoop MapReduce and Apache Spark are two popular choices. Here are some key differences:

Hadoop MapReduce:

• Batch processing framework
• Uses disk-based storage for intermediate data
• Slower processing speed compared to Spark
• Suitable for large-scale data processing

Apache Spark:

• Unified analytics engine
• Uses in-memory processing for faster computations
• Supports real-time stream processing
• More flexible and versatile than MapReduce

Ultimately, the choice between Hadoop MapReduce and Spark depends on the specific requirements of your Big Data project.