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Hadoop is not dead, but its usage is declining as newer technologies like Apache Spark gain popularity due to their faster in-memory processing capabilities and user-friendly APIs. However, Hadoop’s HDFS is still widely used for scalable storage, and YARN for resource management.
**Key Points**:
- **Spark is preferred for** real-time analytics, machine learning, and iterative processing due to its speed and ease of use.
- **Hadoop remains relevant for** large-scale data storage (HDFS) and batch processing tasks.
Transitioning to Spark is beneficial if your needs are real-time processing and advanced analytics, but Hadoop components still play a significant role in big data infrastructure.
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