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Hadoop > Learn Hadoop > What is the difference between Hadoop and Spark?
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What is the difference between Hadoop and Spark?

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Hadoop and Spark are both big data processing frameworks, but they differ in their architecture, processing models, and use cases: Processing model Hadoop processes data in batches, while Spark processes data in real time. Architecture Hadoop uses a distributed framework and file system,...
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Hadoop and Spark are both big data processing frameworks, but they differ in their architecture, processing models, and use cases: Processing model Hadoop processes data in batches, while Spark processes data in real time. Architecture Hadoop uses a distributed framework and file system, while Spark processes data in its own software using RAM. Use cases Hadoop is good for batch processing, data warehousing, and exploratory data analysis, while Spark is good for real-time analytics, interactive data exploration, and iterative algorithms. Scalability Hadoop is easily scalable by adding more nodes, while Spark is more challenging to scale. Security Hadoop has strong security features, storage encryption, and access control, while Spark has basic security. Cost Hadoop is more affordable than Spark. Machine learning Hadoop integrates with external libraries for machine learning, while Spark has built-in machine learning libraries. Fault tolerance Spark manages fault tolerance on the basis of each RDD block, and can regenerate an RDD all the way up to its inception. read less
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Hadoop stores and processes data on external storage. Spark stores and process data on internal memory. Hadoop processes data in batches. Spark processes data in real time.
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