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Experimental Survey on Big Data Frameworks: Context, Motivations, and Best Practices

This experimental survey delves into the realm of Big Data frameworks, exploring their context, challenges, and the best practices associated with them. It highlights the exponential growth in data volume, various data sources and formats, along with the difficulties in scalability and fault tolerance. By categorizing popular frameworks like Hadoop, Spark, Storm, and Flink, the study conducts comparative evaluations in both batch and stream modes using workloads like k-means, WordCount, and PageRank. Utilizing monitoring tools like Kafka, ElasticSearch, and Kibana, the study provides insights into resource consumption, performance variations, and best practices in handling Big Data efficiently.

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Experimental Survey on Big Data Frameworks: Context, Motivations, and Best Practices

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