31 Common Data Engineer Interview Questions & Answers

There are an infinite number of data engineering interview questions that you could be asked, but some are far more common than others. That means if you''re prepared for them, you''ll have a serious advantage when it comes time to interview.

Data engineer interview questions

This list will help you prepare for what''s coming!

1. Can You Tell Me About the Four V’s of Big Data

When an interviewer asks this data engineer interview question, they’re referring to the basic characteristics a big data environment needs to create value. The four V’s are:

  • Velocity: Data must be generated and managed at high velocity.
  • Variety: Organizations thrive with a large variety of data, each with specific treatments.
  • Volume: Big data needs a high volume of data.
  • Veracity: Data must be highly accurate and trustworthy.

2. What Data Does NameNode Store?

NameNode is the heart of a Hadoop Distributed File System (HDFS). It serves as the master of the system. Its purpose is to monitor the metadata and file system tree associated with every folder and file within the HDFS.

NameNode stores metadata for the HDFS. It typically stores data like block information and namespace information. That data is saved into two separate files: “Edit Log” and “Namespace Image.”

3. What XML Config Files are in Hadoop?

XML config files are simple text files that store mapping information for XML data types. There are four XML config files available in Hadoop. They are:

  • Core-site (CORE-SITE.XML)
  • Mapred-site (MAPRED-SITE.XML)
  • Yarn-site (YAR-SITE.XML)

4. What are the Core Methods of Reducer?

The Reducer is the second stage of processing that occurs in Hadoop. It follows the Mapper phase. Reducer takes the output of the Mapper as an input. Then it processes and produces a brand-new output that’s stored in the HDFS.

There are three primary methods of the Reducer.

The first is setup(). It is utilized to configure parameters like input data size and distributed cache protocols.

The second is cleanup(). This method primarily focuses on cleaning up and deleting temporary files.

Finally, there’s reduce(). The reduce() method is the single most important aspect of the Reducer. It is called one time for every key, defining a task for the associated key.

5. Explain to Me What Hadoop Streaming Is

Hadoop streaming is an important utility that comes with Hadoop distribution. It lets you create and run Map/Reduce jobs with any executable or script as the Mapper or Reducer. You can create Map or Reduce tasks before submitting them to any cluster.

With Hadoop streaming, programmers and developers can construct Map/Reduce programs in any language. The utility works well in Python, Perl, Ruby, and more.

Interviewers want to know your familiarity with Hadoop streaming because it’s an efficient utility that plays a critical role in organizations with a big data environment.

6. Can You Explain What Skewed Tables are in Hive?

Skewed tables can help to improve the performance of tables with columns that have skewed values. They’re best utilized when a table has column values in considerable quantities. As a data engineer, demonstrating your understanding of how skewed tables work and when to take advantage of them is a must.

In skewed tables, values often appear in a repeated manner. The more often they repeat, the higher the “skewness.” When using Hive, it’s possible to classify a table as “skewed” as you’re creating it.

When you make skewed tables, the values of the table will be written into different files. Later, the remaining values go into a separate file. Skewed tables store the skew data separately, which is an important distinction to make when answering this data engineering interview question.

7. What is Rack Awareness?

Rack awareness is a unique concept in Hadoop. When rack awareness occurs in Hadoop clusters, the NameNode uses the DataNode to increase incoming network traffic. As this happens, the NameNode continues to perform read or write operations on any file.

The file that the NameNode concurrently performs operations on is the closest to the rack from which the read and write requests come. The NameNode also maintains the rack ID of every DataNode.

The purpose of rack awareness is to optimize reading speed while minimizing the resources used for writing operations. It maximizes network bandwidth within the rack.

8. Explain What Star Schema Is

Star schema is one of two primary schemas used with data modeling. It’s sometimes referred to as “star join schema.” The schema arranges data in a database so that it’s easily understood and analyzed.

This schema is the simplest type of Data Warehouse schema. It’s aptly named for the structure, which resembles a star.

The center of the star typically has one fact table. Multiple associated dimension tables branch from the star’s core.

The star schema is most often used when working with massive amounts of data. Data engineers use it for querying large data sets.

9. Explain What Snowflake Is

The snowflake schema is another method used for data modeling. It’s similar to the more common star schema, but it adds another dimension. It’s more complex, so the structural diagram resembles a multi-branched snowflake.

The fact table at the core remains the same as with the star schema. However, the branching dimension tables are normalized into several layers. The data is structured and split into several tables after normalization.

This schema is less prone to data integrity issues. Because the data is highly structured, it also uses less disk space.

10. What is the Difference Between Star Schema and Snowflake Schema

There are a few key differences between the star schema and the snowflake schema.

The biggest is how data is stored. In the star schema, the data lives in dimensional tables. However, the snowflake schema takes things a bit further by storing each data hierarchy in individual tables.

The star schema offers greater data redundancy compared to the low data redundancy of the snowflake schema.

There’s also a substantial difference in complexity. The star schema facilitates simpler database design and faster cube processing. Meanwhile, the snowflake schema requires complex data-handling storage space and more processing time.

The biggest benefit of snowflake schema is that it’s less prone to data integrity problems.

11. What is a NameNode?

A NameNode is one of the most critical parts of an HDFS. It doesn’t store any actual data, but it stores metadata for the HDFS, such as block and namespace information.

The NameNode helps to track various files across clusters. There’s only one NameNode in an HDFS cluster. So when it crashes, the system may not be available.

12. What is Hadoop?

Hadoop is the gold standard in big data; engineers use it frequently. Interviewers often ask you to define Hadoop to gauge your understanding and ensure you’re qualified to fill positions that use it.

Simply put, Hadoop is an open-source framework utilized for data manipulation and storage. It can also run applications on individual units or clusters.

Hadoop is the primary tool used for processing big data. Developed by Apache Foundation, it has many utilities that improve data application efficiency.

It’s compatible with many types of hardware, supporting faster-distributed processes, and stores data in clusters that stay separate from other operations. One of the biggest advantages of Hadoop is that it’s easy to provision space and resources required for data storage and processing. Hadoop can simultaneously handle limitless jobs, making it a highly efficient framework.

13. What is FSCK?

FSCK stands for File System Check. Originally, it was a method for older Linux-based systems. However, it’s a command still used today to check for errors and file discrepancies.

It’s not a foolproof command. But, FSCK can ensure that metadata stays internally consistent.

14. What are the Collections in Hive?

Hive is a data warehouse system that helps organizations perform analytics on a massive scale. It currently has four collection functions. They are:

  • Map
  • Array
  • Struct
  • Union

15. Why Do You Want to Be a Data Engineer?

In addition to technical questions, interviewers may ask you open-ended questions like this.

The goal of this query is to better understand your motivations and qualifications. There are many reasons why people choose to pursue a career in data engineering. Hiring managers want to see that you’re passionate about the field and committed to using your expertise to help a company’s bottom line.

The best way to answer is to speak confidently about what led you to this unique field. Talk about your experiences with data, what initially sparked your interest, and how you got into data. Review the job description and emphasize your interest in fulfilling the company’s needs.

The goal is to show that you’re not just there for the money. You want to prove that you’re genuinely interested in the complexities of big data and want to continue pushing your career.

16. Share the Most Important Features & Components in Hadoop

There are many reasons why Hadoop is the go-to for big data. Interviewers will ask this data engineering question to dig deeper into your understanding of the framework. When answering, touch on the most important features Hadoop offers. There are five main components to cover.

The first is Hadoop Common. It consists of the libraries and utilities used by Hadoop.

Next is HDFS. HDFS stands for Hadoop Distributed File System and refers to the system Hadoop uses to store data. It’s a distributed file system with high bandwidth to preserve data quality.

The next component you should talk about is MapReduce. MapReduce is a feature based on techniques that facilitate large-scale data processing.

YARN stands for Yet Another Resource Negotiator. In Hadoop, it tackles resource management and allocation.

17. Share Some Design Schemas That are Used in Data Modeling

There are two main design schemas utilized in data modeling. Interviewers ask this question to test general data engineering models.

The first design schema to talk about is the star schema. It features a fact table referenced by several multiple-dimension tables. The dimension tables link to the fact table.

The second schema to discuss is the snowflake schema. It also has a central fact table, but the dimension tables are normalized into several layers.

18. In HDFS, What is a Block and Block Scanner?

Blocks are the smallest unit of data found within a file. It’s a singular entity of data. Hadoops breaks down larger files into smaller blocks. The process allows for more secure storage.

A block scanner validates the blocks in a DataNode. It ensures that the loss-of-blocks produced by Hadoop are successfully put into DataNodes.

19. What Role Does a Context Object Have in Hadoop?

A context object contains task configuration data and interfaces. Its purpose is to allow the Mapper/Reducer to interact with the rest of the Hadoop system.

Applications can take advantage of the context object to report task progress. They use the context object to get system configuration details and view jobs within the constructor.

Typically, context objects are also used to send information to the methods of the Reducer, such as setup(), cleanup(), and map().

20. How Do NameNode and DataNode Communicate?

NameNodes and DataNodes are two critical components of an HDFS. The NameNode only contains metadata. Meanwhile, the DataNodes store the actual data.

These two components communicate through two types of messages.

The first is Block reports. The reports contain a list of data blocks stored on the DataNode.

The second is the Heartbeat signal. It lets the NameNode know that the DataNode is functional. The signal is periodic and determines whether to use the NameNode or not. If there is no Heartbeat signal, the DataNode is likely not working.

21. What is ETL?

This data engineer interview question aims to gauge your understanding of ETL as well as gain more insight into your experience with it.

ETL stands for Extract Transform Load. It’s a data integration process that combines data from multiple sources into a single data store. It’s loaded into a data warehouse or other target system.

When talking about ETL, discuss your experiences with it. Detail the tools you’ve used to validate your knowledge and reassure hiring managers that you’re well-versed in ETL.

22. What Role Does Data Analytics Have in a Successful Company?

Here’s another question that requires you to prove your understanding of data engineering and your role within a company if hired. Data analytics can do a lot to benefit companies. Your job with this question is to show that you understand how your job can lead the organization to success.

There are several points you can bring up.

You can talk about how efficient analytics and data management can lead to structured growth. It can also improve customer value, improve staffing forecasts, cut down production costs, and more.

Discuss the benefits of data analytics and connect your response to the position to demonstrate your knowledge of how data engineering impacts companies.

23. What is Data Engineering?

This data engineering interview question is often one of the first that gets asked. Like other open-ended queries, it’s your chance to emphasize your interest and prove your understanding of this role.

Data engineering is the process of converting raw data into something the organization can actively use to produce growth and success. It’s the act of transforming, profiling, and aggregating large data sets to allow companies to take full advantage of their data assets.

Detail your data engineer experience and review some of the job’s core duties. You want to show that you fully understand what this position entails.

24. How Do You Look at the Structure of a Database with MySQL?

To view the structure of a database with MySQL, you must use the “Describe” command.

The syntax is simple, and you can provide it to prove your knowledge to hiring managers. It’s “DESCRIBE table name;”

25. What is Data Modeling?

Data modeling is one of many duties tasked to data engineers. When you model data, you document complex software design as a diagram. The goal is to make the data easier to understand and visualize.

It’s about creating a conceptual representation of data objects and mapping out how they relate to various other objects and rules. You can use data modeling to show relationships between data entities.

Typically, modeling starts with a conceptual model before you create logical and physical models.

26. What Does a Block Scanner Do with Corrupted Files?

Block scanners verify that loss-of-blocks produced by Hadoop move into DataNodes.

If the block scanner finds corrupted files, the DataNode automatically reports it to the NameNode.

The NameNode then performs a set of functions to determine whether that corrupted file should be removed. It creates replicas of the original corrupted file and searches for a match.

The corrupted file stays if it finds a match between the replicas and the original.

27. What Does the .hiverc File Do in Hive?

The .hiverc file is an initialization file that prepares a system for operation.

Typically, you’d use a .hiverc file to set the initial values of parameters. Whenever you start Command Line Interface (CLI) for Hive, the .hiverc file loads first. It’s the first file to execute when you launch a Hive shell and holds all the preset configurations and parameters.

28. What are *args and **kwargs?

This data engineering interview question is more complex, and it typically comes when you''re trying to land a more advanced data engineering role. Interviewers use it to ensure that you have a full understanding of these functions and why you would use them.

The *args function is for defining an ordered function in the command line. Meanwhile, the **kwargs function represents the unordered functions in a command line. It denotes a set of unordered arguments that are in line to be input into a function.

29. What’s the Difference Between Structured and Unstructured Data

This question lets you demonstrate your knowledge of these two data types and review your experience working with them.

Structured and unstructured data differ in many ways. Generally, unstructured data must be transformed into structured data for full analysis and application.

With structured data, you have a defined storage method through a database management system (DBMS). Unstructured data doesn’t have managed storage.

Unstructured data requires manual data entry and batch processing. However, structured data uses ETL as an integration tool. While scaling is easier with unstructured data than schema scaling with structured data, most companies prefer to work with structured data.

Another difference you can talk about is the standards used. For unstructured data, it’s SMTP, SMS, CVS, and XML. For structured data, it’s ABO.net, SQL, and ODBC.

30. What are Some Crucial Skills Data Engineers Possess?

Here’s another example of a common question interviewers will use to gauge your understanding of data engineering. Every company can have its unique definition of this position. But there are many core skills successful data engineers need.

Look at the job description to understand what the organization wants from an engineer. Use that information to touch on relevant skills like:

  • Data modeling
  • Statistics
  • Database design and architecture
  • Data distribution systems like HDFS
  • Data visualization
  • Mathematics
  • Computing
  • Python, SQL, and HiveQL

31. Name the Usage Modes of Hadoop

You can use Hadoops in three different modes.

The first is the standalone mode. It’s the default mode that Hadoop runs. It’s ideal if you primarily want to debug and don’t use an HDFS.

Next is the pseudo-distributed mode. In this mode, both the NameNode and DataNode live on the same machine. The Hadoop daemons run on a single node, and it’s the mode of choice when you don’t have to worry about resources.

The most commonly used mode is the fully distributed mode. Think of this as the production mode where several nodes run simultaneously. Data moves across several nodes, and processing occurs on each one. You benefit from the reliability, scalability, fault tolerance, and efficiently distributed resources in the fully distributed mode.            


Now that you''re familiar with the most common data engineering interview questions, it''s time to start practicing. Run through any that stumped you and brush up on anything that needs improvement.

While these can seem a bit intimidating at first, the cure is preparation.

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