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What happened to Hadoop?

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A significant contributor to Hadoop's downfall was cloud technology expansion. The vendor market in this niche quickly became crowded. Most of them provided big proprietary data processing services that offered features identical or superior to that of Hadoop.
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"Transforming your struggles into success"

Hadoop is still widely used but has evolved in response to changing technology trends: 1. **Shift to Spark**: Apache Spark has become popular for its faster, in-memory processing capabilities compared to Hadoop's disk-based MapReduce, leading to increased use of Spark for real-time and iterative...
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Hadoop is still widely used but has evolved in response to changing technology trends: 1. **Shift to Spark**: Apache Spark has become popular for its faster, in-memory processing capabilities compared to Hadoop's disk-based MapReduce, leading to increased use of Spark for real-time and iterative data processing. 2. **Cloud Adoption**: Hadoop's ecosystem has increasingly moved to cloud platforms (like AWS EMR, Google Cloud Dataproc, Azure HDInsight), which offer managed services and scalability. 3. **Integration with New Tools**: Hadoop is often used alongside modern tools and frameworks (e.g., Spark, Kafka, Flink) to enhance its capabilities and address limitations. 4. **Decline in Standalone Use**: Hadoop's MapReduce component is less favored for new projects, but HDFS and YARN are still valuable for distributed storage and resource management. Overall, Hadoop remains a key part of the big data landscape but is often integrated with newer technologies and cloud solutions. read less
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