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Hadoop > Learn Hadoop > When is Hadoop MapReduce better than Spark?
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When is Hadoop MapReduce better than Spark?

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Hadoop MapReduce is better than Spark in the following scenarios: 1. *Batch processing*: MapReduce is designed for batch processing and is more efficient for large-scale, long-running jobs. 2. *Complex data processing*: MapReduce is suitable for complex data processing tasks that require multiple...
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Hadoop MapReduce is better than Spark in the following scenarios: 1. *Batch processing*: MapReduce is designed for batch processing and is more efficient for large-scale, long-running jobs. 2. *Complex data processing*: MapReduce is suitable for complex data processing tasks that require multiple stages, such as data aggregation, filtering, and sorting. 3. *Low-level control*: MapReduce provides low-level control over data processing, allowing for fine-grained optimization and customization. 4. *Legacy system integration*: MapReduce is a more mature technology and may be required for integrating with legacy systems or existing Hadoop infrastructure. 5. *Cost-effective*: MapReduce can be more cost-effective for small-scale or infrequent data processing tasks, as it doesn't require the overhead of a Spark cluster. 6. *Specific use cases*: MapReduce is better suited for specific use cases like data warehousing, ETL (Extract, Transform, Load), and data archiving. However, Spark is generally preferred for: 1. *Real-time processing* 2. *Interactive analytics* 3. *Machine learning* 4. *Streaming data* 5. *High-speed data processing* Ultimately, the choice between MapReduce and Spark depends on the specific requirements of your project, including data size, processing complexity, and performance needs. read less
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Spark is excellent for both real-time and batch processing, so you don't need to split tasks across different platforms. It uses in-memory caching and optimized query execution to provide quick results. Hadoop MapReduce is more suitable for batch processing.
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Hadoop MapReduce is better than Spark for batch processing on large-scale data when resources are limited and the workload is primarily disk-based. It’s also suitable for environments where existing Hadoop infrastructure is already in place.
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