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Hadoop > Learn Hadoop > Will Spark overtake Hadoop? Will Hadoop be replaced...
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Will Spark overtake Hadoop? Will Hadoop be replaced by Spark?

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Following are the reasons, Spark can not replace Hadoop: Spark doesn't have storage layer. The best storage layer for Spark is Hadoop's HDFS. Hadoop's Yarn also provides a resource management layer, used by Spark quite efficently.
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"Transforming your struggles into success"

Spark is not likely to completely replace Hadoop, but it has overtaken Hadoop in popularity for many big data applications. **Key Points:** - **Performance**: Spark is faster due to in-memory processing, making it preferred for real-time analytics and machine learning. - **Ease of Use**: Spark's...
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Spark is not likely to completely replace Hadoop, but it has overtaken Hadoop in popularity for many big data applications. 

 

**Key Points:**

- **Performance**: Spark is faster due to in-memory processing, making it preferred for real-time analytics and machine learning.

- **Ease of Use**: Spark's APIs are more user-friendly compared to Hadoop’s MapReduce.

- **Integration**: Spark often runs on Hadoop clusters using HDFS for storage, complementing rather than replacing Hadoop.

 

Hadoop still remains relevant for distributed storage (HDFS) and batch processing with massive datasets. The two technologies are often used together rather than one replacing the other.

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