Top 10 Apache Spark Interview Questions

Introduction

  • You must have looked up Spark Interview questions on the internet. What if we provide you with a tutorial containing Apache Spark interview questions and answers? Won’t that help you in preparing for the Apache Spark interview which so many interviewees dream of cracking? The Spark interview questions and answers that we will be presenting, will not only help you in preparing for the interview but also will make you confident enough to sit for the interview.
  • If you want to know more about the Spark interview questions, all you need to do is read on.
  • In this post, we will be covering the top 10 Apache Spark Interview Questions. Don’t worry! We will also be providing you with the answers.

About Apache Spark

  • Before diving into the Spark interview questions, let us talk a bit about Apache Spark. It is an open-source engine that is used for data processing and that helps workers successfully stream in the following workloads – Spark, Machine Learning, and SQL. It is a cluster computing platform that is used for general purposes. Its design is made in such a way that there is integration with Bid Data tools. One of the reasons why Spark has become so popular is because it is quicker than other Big Data tools. Another reason is that it can work on huge datasets.

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Top 10 Apache Spark Interview Questions with Answers

Now, let’s dive into the Spark Interview Questions. No matter whether you are looking for Spark Interview questions for experienced or for freshers, you have to prepare the following questions as they are very important. Here they are:

Q.1. Why is Apache Spark superior to other Big Data tools like Hadoop-Map Reduce?

Ans. This is one of the most important Spark interview questions. What is exactly asked for in this question are the features of Spark. They are as follows:

 a) Fast – As far as speed is concerned, Spark is a faster data processing tool. It is built on Resilient Distributed Dataset that reduces the time to read and write to discs. Therefore, it is faster than other Big Data tools.

b) Dynamic – It can support multiple languages like Java, Python, and Scala.

c) Enables Data Analytics – Spark has tools for streaming data, Machine Learning, etc., and enables data analytics in a sophisticated way.

d) Stream processing is done in real-time – Another feature that sets Spark apart from the rest is its ability to handle stream processing on a real-time basis.

e) Compatible with Hadoop – Its compatibility with Hadoop is an important feature.

f) Active community – Its community is active and growing.

Q.2. Which are the languages that are supported by Apache Spark?

Ans. This is also one of the most commonly asked Spark interview questions that are asked as one of the Spark interview questions for experienced or for freshers. The language which Apache Spark is written in is Scala but it also supports API for other languages like Python, Java, and R.

Q.3. How is Apache Spark different from Apache Hadoop?

Ans. This is also one of the Spark Interview questions that you may get. Here are the differences:

Hadoop-

 a) Uses local disk to store data

b) Has a slow speed

c) Supports batch processing the most

d) Requirement of external scheduler

e) Latency is high

f) Lack of an in-built interactive mode

Spark –

a) Uses in-memory data storage system

b) Has a quicker speed

c) Supports both batch and real-time processing

d) No requirement for an external scheduler

e) Latency is low

f) Contains an in-built interactive mode

Q.4. Does Spark need a Hadoop cluster to operate?

Ans. This is one of the most important Spark interview questions that you should prepare. The answer to the above question is a simple, “No”. Since both of them are open-source and can be well-integrated with each other, you can use one on top of the other. But, it is not a mandatory requirement.

Q.5. What are the reasons for Apache Spark being faster than Apache Hadoop – Map Reduce?

Ans. Ensure you prepare this when you are preparing Spark interview questions. This is also one of the Spark Interview questions scenario-based. Here are the reasons:

a) Since Spark transforms data in memory, the time to read and write back to disk is reduced. That is not the case with Hadoop-Map Reduce.

b)   Spark completes optimization and computation in one stage only. That is not the case with Hadoop-map Reduce that does the same in multiple stages.

c) Since Apache Spark is built on Scala as against Java, it is faster.

Q.6. What is Apache Spark Core?

Ans. It is the fundamental unit of the Spark project that helps in task dispatching, scheduling, and input-output operations.

Q.7. What is Spark SQL?

Ans. This is also one of the most commonly asked Spark Interview questions. In order to work with structured and semi-structured data, a Spark interface is needed. Spark SQL is that interface that provides the Dataset needed to work with data without any difficulty.

Q.8. What are the disadvantages of Apache Spark?

Ans. Here they are:

a) Lack of real-time processing support

b) Gives permission to a limited number of large files.

c) Lack of a dedicated file management system.

d) In-memory data keeping is costly.

e) Requires manual optimization

f)  Higher latency than other Big Data tools.

g) Lack of support for record-based window

Q. 9. Explain how you will implement SQL in Spark?

Ans. Another of the best Spark interview questions is this one. The answer lies in Spark SQL modules. It not only helps in the integration of relational processing with the functional programming API but also supports the integration of SQL queries with code transformations.

Q.10. What are the different types of Cluster Managers in Spark?

Ans. The different types of Cluster Managers in Spark are:

 a) Apache Mesos

b) Standalone

c) YARN

Closing

These are the Spark Interview questions that you must prepare, come what may. The Spark Interview questions and answers will surely help you to crack the Apache Spark interviews.

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