Answer / Aditya Alok

Apache Spark outperforms Hadoop in certain scenarios due to its in-memory data processing capabilities and faster speed. Here are a few use cases:n1. Real-time streaming and batch processing: Spark's Streaming component processes real-time data streams up to 100 times faster than Hadoop MapReduce.n2. Iterative algorithms: Spark is designed for iterative operations, which makes it more efficient than Hadoop in machine learning applications that require multiple passes over the same dataset.n3. Graph processing: Spark's GraphX API provides a more optimized solution for large-scale graph processing compared to Hadoop's Giraph.n4. SQL queries and data warehousing: Spark's SQL component can execute complex SQL queries faster than Hive on Hadoop, due to its in-memory caching and optimization techniques.

Explain the processing speed difference between Hadoop and Apache Spark?

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Is spark a special attack?

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Please enumerate the various components of the Spark Ecosystem.

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What is meant by in-memory processing in Spark?

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What is write ahead log(journaling)?

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How can data transfer be minimized when working with Apache Spark?

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What is the point of apache spark?

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Why is Transformation lazy in Spark?

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How can you remove the elements with a key present in any other RDD?

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Can you mention some features of spark?

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Is spark written in java?

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Explain first() operation in Apache Spark?

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