Part 2 Interview - Why Spark is often preferred over MapReduce
Mukesh Singh
Why Spark is often preferred over MapReduce?
Apache Spark is an open-source distributed computing system meant for large data processing and analytics. It offers a single engine for distributed data processing that prioritizes speed, simplicity of use, and customization. Spark was developed at UC Berkeley's AMPLab and eventually submitted to the Apache Software Foundation.
1.Performance Spark's in-memory compute paradigm and efficient DAG (Directed Acyclic Graph) execution engine enable much faster processing rates than MapReduce, particularly for iterative algorithms and interactive analytics.
- Ease of Use Spark offers higher-level APIs and libraries (including DataFrame API, Spark SQL, MLlib, and GraphX) that abstract away most of the complexities of distributed data processing. This makes it easy for developers to create and manage big data apps.
3.Versatility Spark's capability for batch processing, real-time streaming, SQL queries, machine learning, and graph processing all inside a single framework makes it an adaptable solution for a wide range of data processing needs. MapReduce, on the other hand, is largely intended for batch processing.
Fault Tolerance with In-Memory Computing: While both Spark and MapReduce offer fault tolerance, Spark's use of resilient distributed datasets (RDDs) and in-memory caching algorithms can result in more effective fault recovery and quicker intermediate data processing.
Community and Ecosystem: Spark has a strong and active open-source community, which has helped to create a rich ecosystem of third-party libraries, connectors, and tools that work with Spark. This ecosystem makes it easy to expand Spark's capabilities and combine it with existing data processing pipelines.
Overall, Apache Spark's speed enhancements, simplicity of use, diversity, and strong community support have contributed to its broad acceptance and preference over classic MapReduce for many large-scale data processing jobs.
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