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Apache Spark Interview Question and Answer (100 FAQ)

Apache Spark Interview Question -Programming, Scenario-Based, Fundamentals, Performance Tuning based Question and Answer

  Bigdata Engineer

   Development, Data Science and AI ML

  Language - English

   Published on 06/2021

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  • Introduction
    01:32
    Preview
  • How to add a index Column in Spark Dataframe?
    04:23
    Preview
  • What are the differences between Apache Spark and Apache Storm?
    02:47
  • How to limit the number of retries on Spark job failure in YARN?
    02:46
  • Is there any way to get Spark Application id, while running a job?
    01:27
  • How to stop a Running Spark Application?
    03:13
  • In Spark Standalone Mode, How to compress spark output written to HDFS
    02:16
  • Is there any way to get the current number of partitions of a DataFrame?
    01:40
  • How to get good performance with Spark.
    02:20
  • Why does a job fail with “No space left on device”, but df says otherwise?
    03:11
  • Where are logs in Spark on YARN? How to view those logs?
    01:01

Description

Apache Spark Interview Questions has a collection of 100 questions with answers asked in the interview for freshers and experienced (Programming, Scenario-Based, Fundamentals, Performance Tuning based Question and Answer). This course is intended to help Apache Spark Career Aspirants to prepare for the interview.

We are planning to add more questions in upcoming versions of this course.

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.


Course Consist of the Interview Question on the following Topics

  • RDD Programming Spark basics - RDDs ( Spark Core)

  • Spark SQL, Datasets, and DataFrames: processing structured data with relational queries 

  • Structured Streaming: processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams)

  • Spark Streaming: processing data streams using DStreams (old API)

  • MLlib: applying machine learning algorithms

  • GraphX: processing graphs

What Will I Get ?

  • By attending this course you will get to know frequently and most likely asked Programming, Scenario based, Fundamentals, and Performance Tuning based Question asked in Apache Spark Interview along with the answer This will help Apache Spark Career Aspir

Requirements

  • Apache Spark basic fundamental knowledge is required
  • This course is designed for Apache Spark Job seeker with 6 months to 4 years of Experience in Apache Spark Development and looking out for new job as Spark Developer,Bigdata Engineers or Developers, Software Developer, Software Architect, Development Man
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