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List some use cases where Spark outperforms Hadoop in processing.
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Answer Posted / Aditya Alok
Apache Spark outperforms Hadoop in certain scenarios due to its in-memory data processing capabilities and faster speed. Here are a few use cases:n1. Real-time streaming and batch processing: Spark's Streaming component processes real-time data streams up to 100 times faster than Hadoop MapReduce.n2. Iterative algorithms: Spark is designed for iterative operations, which makes it more efficient than Hadoop in machine learning applications that require multiple passes over the same dataset.n3. Graph processing: Spark's GraphX API provides a more optimized solution for large-scale graph processing compared to Hadoop's Giraph.n4. SQL queries and data warehousing: Spark's SQL component can execute complex SQL queries faster than Hive on Hadoop, due to its in-memory caching and optimization techniques.
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