In Spark, you can basically do everything from single code or console (PySpark or Scala console) and get the results immediately.And quite often, translating the output of one MapReduce job into the input of another MapReduce job might require writing another code because Oozie may not suffice. In case of Spark, this is avoided using RDDs and utilizing memory (RAM). Between two consecutive MapReduce jobs, the data has to be written to HDFS and read from HDFS. This mechanism is very time consuming and MapReduce tasks have heavy latency. In Hadoop MapReduce, you write many MapReduce jobs and then tie these jobs together using Oozie/shell script.If run in-memory it is 100x faster than Hadoop MapReduce. What is Apache Spark and what are the benefits of Spark over MapReduce? Hope these Apache Spark interview questions and answers guide will help you in getting prepared for your next interview.
Our experts have curated these questions to give you an idea of the type of questions which may be asked in an interview. There is a massive growth in the big data space, and job opportunities are skyrocketing, making this the perfect time to launch your career in this space. Here are the top Apache Spark interview questions and answers.
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