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.

What is the default spark executor memory?

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Which one will you choose for a project –Hadoop MapReduce or Apache Spark?

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Why we need compression and what are the different compression format supported?

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Does spark use mapreduce?

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Does spark use tez?

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Is scala required for spark?

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What happens when an action is executed in spark?

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Explain how can spark be connected to apache mesos?

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What is Speculative Execution in Apache Spark?

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What are the various levels of persistence in Apache Spark?

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Explain about the major libraries that constitute the Spark Ecosystem?

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Can I learn spark without hadoop?

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