Latest 100 Hadoop and Spark interview Questions and Answers in Big Data





Nowadays interviewer asked below Spark interview questions for Data Engineers, Hadoop Developers & Hadoop Admins. Below are basic and intermediate Spark interview questions.

Latest 100 Hadoop and Spark Interview Questions and Answers

1. What is the major difference between Spark and Hadoop?

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2. What are the differences between functional and imperative languages, and why is functional programming important?

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3. What is a resilient distributed dataset(RDD), explain showing diagrams?

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4. Explain transformations and actions in the context of RDDs?

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5. What are the Spark streaming use cases?

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6. What is the lazy evaluation and why is it useful?

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7. What is Parallel Collection RDD?

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8. Explain how ReduceByKey and GroupByKey work?

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9What is the common workflow of a Spark Program?

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10. Explain the Directed Acyclic Graph? Difference between DAG and Lineage Graph?




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Spark interview questions

11. What are the transformations and actions that you have used in Spark in your project?

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12. How can you minimize data transfers when working within the Spark?

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13. What is a lineage graph?

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14.Describe the major libraries that constitute the Spark Ecosystem

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15. What are the pair RDDs?

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16. What are the different file formats that can be used in Hadoop and Spark?

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17. Which Storage Level to choose in your’s project?

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18. What is the difference between cache() and persist()? Explain it with an example?

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19. What are the various levels of persistence in Spark?

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20What are the advantages and drawbacks of RDD? explain it?

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21. Why Dataset is preferred over RDDs?

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22. How to share data from Spark RDD between two applications?

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23. Explain Apache Spark provide checkpointing?

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24. Explain Apache Spark caching memory with example?

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25. What is the function of Block manager in Spark

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26. Why does Spark SQL consider the support of indexes unimportant?

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27. How to convert existing UDTFs in Hive to Scala functions and use them from Spark SQL to explain with example?

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28. Why use data frames and datasets when we have RDD?

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29. What is a Catalyst and how does it work?

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30. What are the top challenges developers face while writing Spark applications?

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31. Explain the difference in implementation between DataFrames and DataSet?

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32. How is memory handled in DataSets?

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33. What are the limitations of the dataset?

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34. What are the contentions with memory?

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35. Show command to run Spark in YARN client mode?

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36. Show command to run Spark in YARN cluster mode?

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37.What is Standalone and YARN mode?

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38. Explain client mode and cluster mode in Spark?

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39. Which cluster managers are supported by Spark?

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40. What is Executor memory?

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41. What is DStream and what is the difference between batch and DStream in Spark Streaming?

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42. How does SparkĀ  Streaming work?

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43.Difference between map () and flatMap()?

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44. What is reducing () actions, Is there any differences between reducing () and reduceByKey()?

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45. What is the disadvantage of reducing () action and how can we overcome this limitation?

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46. What are Accumulators and when are accumulators truly reliable?

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47. What are the Broadcast Variables and what advantages do they provide?

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48. What is a driver?

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49. What is the piping? Demonstrate an example of a data pipeline?

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50. What does a Spark Engine do?

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51. What are the steps that occur when you run a Spark application on the cluster?

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52. What is a schema RDD/Dataframe?

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53. What are the Row objects?

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54. How does Spark achieve fault tolerance?

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55. What parameter is set if cores need to be defined across executors?

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56. Name a few Spark master system properties?

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57. Define partitions in reference to Spark implementation?

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58.Difference between how Spark and MapReduce manage cluster resources under YARN?

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59. What is GraphX and what is PageRank?

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60. What does MLib do?

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61. What is a Parquet file? Explain it?

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62. What is schema evolution and what is its disadvantage, explain schema merging in reference to parquet file?

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63. How will Spark replace MapReduce?

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64. Why is Parquet & AVRO file used for Spark SQL?

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65. Explain Spark executors? with diagram?

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66. Name the different types of cluster managers in Spark?

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67. How many ways to create RDDS, with example?

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68. How you flatten rows in Spark? Explain with example?

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69. What is Hive on Spark?

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70. Briefly, explain about Spark Streaming Architecture?

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71. What are the types of Transformations on DStreams?

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72. What is Receiver in Spark Streaming, and can you build customer receivers?

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73. Explain the process of Live Streaming storing Dstreams data to the database?

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74. How is Spark streaming fault-tolerant?

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75. Explain the transform() method used in DStream?

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76. What file systems does support Spark in your project?

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77. How is data security achieved in Spark in your current Hadoop cluster?

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78. What is Security? Explain Kerberos security?

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79. Name various types of distributing that Spark supports?

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80. Explain some examples of queries using the Scala DataFrame API?

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81. What are the most important factors you want to consider when you start the machine learning project?

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82. What are the conditions where the Spark driver can parallelize dataSets as RDDs?

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83. Can repartition() operation decrease the number of partitions?

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84. What is the drawback of repartition() and coalesce() operation?

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85. Consider the following code in Spark, what are the final values in fVal variable?

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86. Scala pattern matching, show various ways code can be written?

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87. In a joint operation, for example, Val join Val =rddA.join(rddB) will generate partition?

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88.If we want to display just the schema of the data frame/dataset what method is called?

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89. Show various implementations for the following query in Spark?

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90. What are the most important factors you want to consider when you start the machine learning project?

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91. As a data scientist, which algorithm would you suggest if legal aspects and ease of explanation to no technical people are the main criteria?

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92. For the supervised learning algorithm, what percentage of data is split between training and test dataset?

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93. Compare the performance of Parquet and Avro file formats and their usage in the context of Spark?

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94. Spark master exposes a set of REST APIs to submit and monitor applications. Which data format is used for these web services?

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95. When you should not use Spark?

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96. Can you use Spark to access and analyze data stored in Cassandra databases?

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97. With which mathematical properties can you achieve parallelism?

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98. What are the various types of partitioning in Apache Spark?

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99. How to set partitioning for data in Apache Spark?

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100. Explain your project architecture? how to spark involvement for data processing?

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