What is parallelism in data processing?
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What is parallelism in data processing?
Parallelism in data processing refers to the simultaneous execution of multiple processes or tasks to improve performance.
How does it differ from sequential processing?
In sequential processing, tasks are completed one after another, while parallel processing allows multiple tasks to be executed at the same time.
What are some common applications of parallel processing?
Common applications include data analysis, image processing, and machine learning, where large datasets can be processed faster.
What technologies are used for parallel data processing?
Technologies like Apache Hadoop, Apache Spark, and distributed computing frameworks are commonly used for parallel data processing.
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