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What makes Apache Spark good at low-latency workloads like graph processing and machine learning?
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Answer / Meenakshi
Apache Spark excels at low-latency workloads because of its in-memory caching, efficient data shuffling, and support for iterative algorithms. In-memory caching allows frequently used data to be kept in memory for quick access, reducing the need for disk I/O and improving performance. Efficient data shuffling ensures that data is moved between tasks efficiently, minimizing latency. Finally, Spark's support for iterative algorithms makes it well-suited for graph processing and machine learning tasks where multiple iterations are often required.
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