Top 45 Spark interview questions to find the best data engineers

Apache Spark is a powerful tool for processing and analyzing big data, making Spark skills and experience a hot commodity.

If you rely on Spark for your big data projects, finding the right talent who’s proficient with it is as important as it is tricky. 

Without the right experts on board, your projects might not live up to their potential, resulting in less impactful (or sometimes even plainly wrong) insights.

To help you spot the best Apache Spark talent, we've put together a list of 45 Apache Spark interview questions and built a comprehensive Spark test. 

Read on to find out more.

How can you assess a candidate’s Spark skills?

Proficiency in Apache Spark is key for roles that require processing large volumes of data quickly and efficiently: data engineers, data scientists, big data architects, machine learning engineers, and more. 

If your company is working with big data, you need to know how to assess candidates’ Spark skills accurately and without letting bias seep into your hiring process. 

But how? 

The best way to assess data engineering skills is to: 

• Use a pre-employment skills assessment that includes an Apache Spark test to check applicants’ proficiency with the tool

• Conduct thorough interviews with top candidates using targeted Spark interview questions 

If you're ready to begin, check out TestGorilla’s test library to select the best tests for your open position. 

Here are our top suggestions if you’re hiring a data engineer: 

• Apache Spark for Data Engineers: Evaluate candidates' foundational knowledge of Apache Spark with this test

• MATLAB: Make sure candidates are familiar with the MATLAB programming language and know how to use it efficiently in big-data projects

• Fundamentals of Statistics and Probability: This test is perfect for any role requiring a strong understanding of key statistical concepts

• Data Science: Assess candidates’ knowledge of statistics, machine learning, deep learning, and neural networks

Combine those with cognitive ability or personality and culture tests for a 360-degrees evaluation of the talent in your talent pipeline. 

To help you with the second step of the process, we’ve prepared 45 Apache Spark interview questions, plus sample answers to 20 of them. 

Top 20 Spark interview questions to hire the best talent

In this section, you’ll find our selection of the best interview questions to evaluate candidates’ proficiency in Apache Spark. To help you with this task, we’ve also included sample answers to which you can compare applicants’ responses. 

1. What are the key features of Apache Spark?

Skilled candidates will be well-aware of Spark’s most important features. Expect them to talk about the tool’s: 

• Ability to handle batch and real-time data processing efficiently

• Speed, which is significantly better than Hadoop MapReduce due to in-memory computation

• Ease of use, with APIs available in Java, Scala, Python, and R

• Scope of applications, providing libraries for SQL, streaming, machine learning, and graph processing

The best candidates will also mention Spark's support for multiple data sources, like HDFS, Cassandra, HBase, and S3. If you’re hiring for more senior roles, ask for examples where they've used these features in their projects.

2. What are some alternatives to Spark for big data processing?

Alternatives to Spark – and their notable benefits – include: 

• Apache Flink, which is renowned for its real-time streaming capabilities

• Apache Storm, which is best for stream processing with a focus on low latency

• Hadoop MapReduce, which data engineers and analysts can use for scalable batch processing

Look for applicants who can explain the strengths and weaknesses of each tool in different use cases.

3. How is Spark different from Hadoop MapReduce?

Expect detailed answers here. Candidates should mention that Spark performs computations in memory, leading to faster processing times compared to Hadoop MapReduce, which writes intermediate results to disk. 

They might also note that Spark provides a richer set of APIs and supports data processing tasks beyond MapReduce patterns, such as interactive queries and stream processing. Another key difference to listen for is Spark's ability to run on top of Hadoop and utilize YARN for resource management, making it more versatile.

4. What are DataFrames in Spark and how are they different from RDDs?

DataFrames are distributed collections of data organized into named columns, similar to tables in a relational database. They offer richer optimizations through Spark SQL's execution engine, like catalyst query optimization and Tungsten execution engine for off-heap data storage. 

In contrast, RDDs (Resilient Distributed Datasets) are lower-level APIs that provide more control over data and enable fine-grained transformations. 

Top candidates will explain when they’d use each type, for example: 

• DataFrames for higher-level abstractions and optimizations

• RDDs for more complex, custom transformations that aren't easily expressed in SQL

5. Can you describe the Spark ecosystem and its components?

The Spark ecosystem is very comprehensive, so look for answers that go into the details. Candidates should mention:

• Spark Core for basic functionality like task scheduling and I/O operations

• Spark SQL for processing structured data

• Spark Streaming for processing real-time data streams

• MLlib for machine learning

• GraphX for graph processing

Applicants might also mention newer additions like Structured Streaming for a higher-level API for stream processing. 

The best answers will include an explanation of how these components interact and complement each other in a data analysis pipeline.

6. How does Spark achieve fault tolerance?

Spark relies on RDDs (Resilient Distributed Datasets) for fault tolerance. Candidates should explain that RDDs are immutable and distributed, allowing Spark to recompute lost data in the event of a node failure. 

They might also mention checkpointing and lineage, where Spark keeps track of the sequence of transformations applied to create an RDD, so it can rebuild it if needed. 

The best answers will include examples of how these mechanisms have helped maintain data integrity and system reliability in candidates’ past projects.

7. How have you used Spark in your past projects? Give us a few examples.

Expect candidates to provide specific examples of their hands-on experience with Spark. 

Look for descriptions of projects where they used Spark for: 

• Batch processing of large datasets

• Real-time stream processing

• Machine learning model training

• Interactive data analysis

The more details they give you about each project, the better – especially if it aligns with the projects your team is working on. 

For instance, a candidate might describe using Spark SQL to explore and aggregate data exploration in a project analyzing user behavior, or tell you how they used Spark Streaming to process and analyze real-time logs. 

8. Can you explain a data pipeline you designed using Spark?

Look for a step-by-step breakdown of a data pipeline, showing the candidate's ability to design and implement end-to-end solutions using Spark. 

They should mention the following steps: 

• Data ingestion, for example from Kafka or HDFS

• Data processing using Spark SQL or DataFrames for transformations

• Data output or storage, like writing to a database or a file system

Bonus points if they discuss challenges they faced during the project, such as handling skewed data or needing to optimize for performance, and how they handled them.

9. What are the different data sources that Spark supports?

Spark supports various data sources, such as: 

• HDFS

• Cassandra

• HBase

• S3

• JDBC databases

• Kafka

Look for answers that go into the details of how Spark interacts with different sources, perhaps mentioning the use of Spark SQL for connecting to relational databases or the Hadoop InputFormat for reading from HDFS. 

10. How do you read data from a CSV file into a Spark DataFrame?

Look for a concise, technical explanation here. Candidates should mention using the spark.read.csv("path_to_csv") method, explaining options to specify schema, handle header rows, set delimiters, or manage null values. 

This question tests basic technical know-how, but the best candidates will also briefly touch on why they might choose certain options based on the data's characteristics.

11. Explain the concept of transformations and actions in Spark.

Candidates should clearly differentiate between the two:

• Transformations create new RDDs or DataFrames from existing ones without immediate execution; examples include map, filter, and join

• Actions trigger computation and produce output; examples include count, collect, and save

A strong candidate would also give details on Spark's lazy evaluation model, where transformations only execute when an action is called. This optimizes the overall execution plan.

12. How do you perform SQL queries in Spark?

This question assesses the candidates’ knowledge of Spark's higher-level abstraction for structured data processing.

Candidates should talk about Spark SQL and its ability to execute SQL queries directly on DataFrames with the spark.sql("SELECT * FROM table") method. They might also mention creating temporary views (DataFrame.createOrReplaceTempView("tableName")) to run SQL queries on DataFrames. 

13. Compare broadcast joins and shuffle joins in Spark.

Look for answers that cover the basic mechanics and ideal use cases for each type of join: 

• Broadcast joins involve sending a small DataFrame to all nodes in the cluster to join with a larger DataFrame. This is ideal for small-to-medium sized datasets. 

• Shuffle joins involve redistributing both DataFrames across nodes based on the join key. That’s necessary for large datasets but requires more resources.

Candidates might also explain how they’d make a choice based on the size of the datasets and the network’s bandwidth.

14. What are UDFs (User Defined Functions) in Spark? How do you use them?

User Defined Functions (UDFs) enable users to extend the capabilities of Spark SQL by writing custom functions for tasks that require a logic not available in Spark. 

Candidates should explain how to define UDFs in Scala, Python, or Java and use them for Spark SQL queries. Skilled applicants should also know that UDFs might have performance issues, because they run slower than built-in functions. This means that using higher-order functions whenever possible is the best approach.

15. What is Spark Streaming and how does it work?

Spark Streaming is a Spark component that enables the processing of live data streams. It works by continuously ingesting data in small batches and processing those batches to produce a final stream of results. 

Candidates might mention the sources from which they could stream data (like Kafka, Flume, and Kinesis) and the types of operations they can perform on the streamed data, such as map, reduce, and window operations.

To evaluate candidates’ proficiency in Apache Kafka, check out our Kafka interview questions.

16. What are DStreams?

Discretized Streams (DStreams) are the fundamental abstraction in Spark Streaming and a continuous stream of data. They’re built on RDDs (Resilient Distributed Datasets), meaning they benefit from Spark's fault tolerance and scalability. 

Users can apply transformations on DStreams to create new DStreams; applicants might also explain that actions trigger the execution of the streaming computation, leading to output.

17. What are the various cluster managers supported by Spark?

Spark can run on different hardware or cloud environments. Look for answers that mention Apache Mesos (its standalone cluster manager), Hadoop YARN, and Kubernetes. 

Bonus points go to candidates who know the pros and cons of each cluster manager, such as YARN's integration with Hadoop ecosystems or Kubernetes' container orchestration features.

18. How does Spark achieve parallelism?

Spark uses RDDs and DataFrames to parallelize data processing across a cluster. 

Candidates might explain the role of partitions in dividing the data into chunks that can be processed in parallel and talk about how the Spark scheduler optimizes task distribution among the nodes. 

Top answers will include details on adjusting the level of parallelism for operations by specifying the number of partitions.

19. How can you manage skewed data in Spark?

The best strategies to manage skewed data in Spark include: 

• Salting keys to break up large partitions

• Using custom partitioning to distribute data more evenly

• Using broadcast joins to avoid shuffling large datasets

Candidates might also say they’d use monitoring tools or Spark UI to identify skewed data.

20. How would you build a machine learning model using Spark MLlib?

Building a machine learning model in Spark involves: 

• Data preprocessing, i.e. handling missing values, extracting features, and transforming data

• Choosing the best algorithm

• Training the model

• Evaluating its performance using the MLlib library

Skilled applicants might mention using DataFrames for ML pipelines, cross-validation for model tuning, and saving/loading models. 

25 additional Spark interview questions you can ask candidates

If you need more questions to evaluate your candidates’ skills, check out our selection of the best data engineer interview questions. 

And, if you’re looking for more questions related to Apache Spark, here are 25 extra questions you can ask:

1. What are Spark’s security features?

2. What are some common mistakes developers make when using Spark?

3. What is a SparkSession? How do you use it?

4. What is the Catalyst optimizer in Spark SQL?

5. How can you optimize Spark SQL queries?

6. Explain memory management in Spark.

7. How can you handle late-arriving data in Spark Streaming?

8. What are the differences between Spark Streaming and Structured Streaming?

9. What is speculative execution in Spark?

10. How do you deploy a Spark application?

11. What are some common performance issues in Spark applications?

12. How do you tackle performance issues in Spark applications?

13. Explain the role of partitioning in Spark’s performance.

14. How do you decide the number of partitions for an RDD?

15. What is MLlib in Spark?

16. How can you handle missing data in Spark ML?

17. Explain model evaluation metrics in Spark ML.

18. What is the process of submitting a Spark job?

19. What considerations do you need to make when running Spark on cloud services?

20. How do you debug a Spark application?

21. What are some strategies for ensuring high availability of Spark applications?

22. Describe a case where you would use Spark over other big data technologies.

23. How would you use Spark with real-time data processing?

24. How does Spark integrate with Hadoop ecosystem components?

25. How do you migrate a Spark application to a newer version?

Hire the best data engineers with top Apache Spark skills

Evaluating candidates’ experience with Apache Spark is not a difficult task, if you have the right tools at hand. 

Now that you know what Spark interview questions to ask, you can start building your first skills assessment to evaluate candidates and shortlist the ones to interview. Don’t forget to include an Apache Spark test in it to make sure your applicants have what it takes to succeed in the role you’re hiring for. 

Book a free demo with a member of our team to see if TestGorilla is right for you – or check out our free plan to jump right in and start assessing candidates’ skills today.