Top 50 Data Engineer Interview Questions and Answers

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We are sure you all have this question in mind – How to prepare for a Data Engineer interview? This Top Data Engineer interview questions blog is carefully curated with questions that commonly appear in interviews across all companies. Following through and understanding the questions will help you grasp the concepts faster and be more confident in the interviews that you’re preparing for.

Q1. What is Data Engineering?
Q2. Define Data Modeling.
Q3. What are some of the design schemas used when performing Data Modeling?
Q4. What are the differences between structured and unstructured data?
Q5. What is Hadoop, in brief?
Q6. What are some of the important components of Hadoop?
Q7. What is a NameNode in HDFS?
Q8. What is Hadoop Streaming?
Q9. What are some of the important features of Hadoop?
Q10. What are the four Vs of Big Data?

This Top Data Engineer Interview Questions and Answers blog is divided into three sections as shown below:
1. Basic

2. Intermediate

3. Advanced

Basic Interview Questions

1. What is Data Engineering?

Data Engineering is a term one uses when working with data. The main process of converting the raw entity of data into useful information that can be used for various purposes is called Data Engineering. This involves the Data Engineer working with the data by performing data collection and research on the same.

2. Define Data Modeling.

Data modeling is the simplification of complex software designs by breaking them up into simple diagrams that are easy to understand, and it does not require any prerequisites for the same. This provides numerous advantages as there is a simple visual representation between the data objects involved and the rules associated with them.

3. What are some of the design schemas used when performing Data Modeling?

There are two schemas when one works with data modeling. They are:

• Star schema
• Snowflake schema

4. What are the differences between structured and unstructured data?

5. What is Hadoop? Explain briefly.

Hadoop is an open-source framework, which is used for data manipulation and data storage, as well as for running applications on units called clusters. Hadoop has been the gold standard of the day when it comes to working with and handling Big Data.

The main advantage is the easy provision of the huge amounts of space needed for data storage and a vast amount of processing power to handle limitless jobs and tasks concurrently.

6. What are some of the important components of Hadoop?

There are many components involved when working with Hadoop, and some of them are as follows:

• Hadoop Common: This consists of all libraries and utilities that are commonly used by the Hadoop application.
• HDFS: The Hadoop File System is where all the data is stored when working with Hadoop. It provides a distributed file system with very high bandwidth.
• Hadoop YARN: Yet Another Resource Negotiator is used for managing resources in the Hadoop system. Task scheduling can also be performed using YARN.
• Hadoop MapReduce: It is based on techniques that provide user access to large-scale data processing.

7. What is a NameNode in HDFS?

NameNode is one of the vital parts of HDFS. It is used as a way to store all the HDFS data and, at the same time, keep track of the files in all clusters as well. 

However, you must know that the data is actually stored in the DataNodes and not in the NameNodes.

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8. What is Hadoop Streaming?

Hadoop streaming is one of the widely used utilities provided by Hadoop for users to easily create maps and perform reduction operations. Later, this can be submitted into a specific cluster for usage.

9. What are some of the important features of Hadoop?

• Hadoop is an open-source framework.
• Hadoop works on the basis of distributed computing.
• It provides faster data processing due to parallel computing.
• Data is stored in separate clusters away from the operations.
• Data redundancy is given priority to ensure no data loss.

10. What are the four Vs of Big Data?

The following forms to be the vital foundation of Big Data:

• Volume
• Variety
• Velocity
• Veracity

11. What is Block and Block Scanner in HDFS?

Block is considered as a singular entity of data, which is the smallest factor. When Hadoop encounters a large file, it automatically slices the file into smaller chunks called blocks.

A block scanner is put into place to verify whether the loss-of-blocks created by Hadoop is put on the DataNode successfully or not.

12. How does a Block Scanner handle corrupted files?

• When the block scanner comes across a file that is corrupted, the DataNode reports this particular file to the NameNode.
• The NameNode then processes the file by creating replicas of the same using the original (corrupted) file.
• If there is a match between the replicas created and the replication block, then the corrupted data block is not removed.

13. How does the NameNode communicate with the DataNode?

The NameNode and the DataNode communicate via messages. There are two messages that are sent across the channel:

• Block reports
• Heartbeats

14. What is meant by COSHH?

COSHH is the abbreviation for Classification and Optimization-based Scheduling for Heterogeneous Hadoop systems. As the name suggests, it provides scheduling at both the cluster and the application levels to directly have a positive impact on the completion time for jobs.

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15. What is Star Schema, in brief?

Star schema is also called the star join schema, which is one of the simple schemas in the concept of Data Warehousing. Its structure resembles a star that consists of fact tables and associated dimension tables. The star schema is widely used when working with large amounts of data.

16. Explain Snowflake in brief.

The snowflake schema is a primary extension of the star schema with the presence of more dimensions. It is spanned across as the structure of a snowflake, hence, the name. Data is structured here and split into more tables after normalization.

17. State the differences between Star Schema and Snowflake Schema.

18. Name the XML configuration files present in Hadoop.

Following are the XML configuration files available in Hadoop:

• Core-site
• Mapred-site
• HDFS-site
• YARN-site

19. What is the meaning of FSCK?

FSCK is also known as the File System Check, which is one of the important commands used in HDFS. It is primarily put to use when you have to check for problems and discrepancies in files.

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Intermediate Interview Questions

20. What are some of the methods of Reducer?

Following are the three main methods involved with reducer:

• setup(): This is primarily used to configure input data parameters and cache protocols.
• cleanup(): This method is used to remove the temporary files stored.
• reduce(): The method is called one time for every key, and it happens to be the single most important aspect of the reducer on the whole.

21. What are the different usage modes of Hadoop?

Hadoop can be used in three different modes. They are:

• Standalone mode
• Pseudo distributed mode
• Fully distributed mode

22. How is data security ensured in Hadoop?

Following are some of the steps involved in securing data in Hadoop:

• You need to begin by securing the authentic channel that connects clients to the server.
• Second, the clients make use of the stamp that is received to request a service ticket.
• Lastly, the clients use the service ticket as a tool for authentically connecting to the corresponding server.

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23. Which are the default port numbers for Port Tracker, Task Tracker, and NameNode in Hadoop?

• Job Tracker has the default port: 50030
• Task Tracker has the default port: 50060
• NameNode has the default port: 50070

24. How does Big Data Analytics help increase the revenue of a company?

Data Analytics helps the companies of today’s world in numerous ways. Following are the foundational concepts in which it helps:

• Effective use of data to relate to structured growth
• Effective customer value increase and retention analysis
• Manpower forecasting and improved staffing methods
• Bringing down the production cost majorly

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25. In your opinion, what does a Data Engineer majorly do?

A Data Engineer is responsible for a wide array of things. Following are some of the important ones:

• Handling data inflow and processing pipelines
• Maintaining data staging areas
• Responsible for ETL data transformation activities
• Performing data cleaning and the removal of redundancies
• Creating ad-hoc query building operations and native data extraction methods

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26. What are some of the technologies and skills that a Data Engineer should possess?

Following are the important technologies that a Data Engineer must be proficient in:

• Mathematics (probability and linear algebra)
• Summary statistics
• Machine Learning
• R and SAS programming languages
• Python
• SQL and HiveQL

Followed by this, a Data Engineer must also have good problem-solving skills and analytical thinking ability.

27. What is the difference between a Data Architect and a Data Engineer?

A Data Architect is a person who is responsible for managing the data that comes into the organization from a variety of sources. Data handling skills such as database technologies are a must-have skill of a Data Architect. The Data Architect is also concerned with how changes in the data will lead to major conflicts in the organization model.

Now, a Data Engineer is the person who is primarily responsible for helping the Data Architect with setting up and establishing the Data Warehousing pipeline and the architecture of enterprise data hubs.

28. How is the distance between nodes defined when using Hadoop?

The distance between nodes is the simple sum of the distances to the closest corresponding nodes. The getDistance() method is used to calculate these distances.

29. What is the data stored in the NameNode?

NameNode primarily consists of all of the metadata information for HDFS such as the namespace details and the individual block information.

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30. What is meant by Rack Awareness?

Rack awareness is a concept in which the NameNode makes use of the DataNode to increase the incoming network traffic while concurrently performing reading or writing operations on the file, which is the closest to the rack from which the request was called for. 

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31. What is a Heartbeat message?

Heartbeat is one of the two ways the DataNode communicates with the NameNode. It is an important signal which is sent by the DataNode to the NameNode in a structured interval to show that it is still operational.

32. What is the use of a Context Object in Hadoop?

A context object is used in Hadoop, along with the mapper class, as a means of communication with the other parts of the system. System configuration details and jobs present in the constructor are obtained easily using the context object.

It is also used to send information to methods such as setup(), cleanup(), and map().

33. What is the use of Hive in the Hadoop ecosystem?

Hive is used to provide the user interface to manage all the stored data in Hadoop. The data is mapped with HBase tables and worked upon, as and when needed. Hive queries (similar to SQL queries) are executed to be converted into MapReduce jobs. This is done to keep the complexity under check when executing multiple jobs at once.

34. What is the use of Metastore in Hive?

Metastore is used as a storage location for the schema and Hive tables. Data such as definitions, mappings, and other metadata can be stored in the metastore. This is later stored in an RDMS when required.

Next up on this compilation of top Data Engineer interview questions, let us check out the advanced set of questions.

Advanced Interview Questions

35. What are the components that are available in the Hive data model?

Following are some of the components in Hive:

• Buckets
• Tables
• Partitions

36. Can you create more than a single table for an individual data file?

Yes, it is possible to create more than one table for a data file. In Hive, schemas are stored in the metastore. Therefore, it is very easy to obtain the result for the corresponding data.

37. What is the meaning of Skewed tables in Hive?

 

Skewed tables are the tables in which values appear in a repeated manner. The more they repeat, the more the skewness.

Using Hive, a table can be classified as SKEWED while creating it. By doing this, the values will be written to different files first, and later, the remaining values will go to a separate file.

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38. What are the collections that are present in Hive?

Hive has the following collections/data types:

• Array
• Map
• Struct
• Union

 

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39. What is SerDe in Hive?

SerDe stands for Serialization and Deserialization in Hive. It is the operation that is involved when passing records through Hive tables.

The Deserializer takes a record and converts it into a Java object, that is understood by Hive.

Now, the Serializer takes this Java object and converts it into a format that is processable by HDFS. Later, HDFS takes over for the storage function.

40. What are the table creation functions present in Hive?

Following are some of the table creation functions in Hive:

• Explode(array)
• Explode(map)
• JSON_tuple()
• Stack()

41. What is the role of the .hiverc file in Hive?

The role of the .hiverc file is initialization. Whenever you want to write code for Hive, you open up the CLI (command-line interface), and whenever the CLI is opened, this file is the first one to load. It contains the parameters that you initially set.

42. What are *args and **kwargs used for?

The *args function lets users define an ordered function for usage in the command line, and the **kwargs function is used to denote a set of arguments that are unordered and in line to be input to a function.

43. How can you see the structure of a database using MySQL?

To see the structure of a database, the describe command can be used. The syntax is simple:

describe tablename;

44. Can you search for a specific string in a column present in a MySQL table?

Yes, specific strings and corresponding substring operations can be performed in MySQL. The regex operator is used for this purpose.

45. In brief, what is the difference between a Data Warehouse and a Database?

When working with Data Warehousing, the primary focus goes on using aggregation functions, performing calculations, and selecting subsets in data for processing. With databases, the main use is related to data manipulation, deletion operations, and more. Speed and efficiency play a big role when working with either of these.

46. Have you earned any sort of certification to boost your opportunities as a Data Engineer?

Interviewers look for candidates who are serious about advancing their career options by making use of additional tools like certifications. Certificates are strong proof that you have put in all efforts to learn new skills, master them, and put them into use at the best of your capacity. List the certifications, if you have any, and do talk about them in brief, explaining what all you learned from the program and how it’s been helpful to you so far.

47. Do you have any experience working in the same industry as ours before?

This question is a frequent one. It is asked to understand if you have had any previous exposure to the environment and work in the same. Make sure to elaborate the experience you have, with the tools you’ve used and the techniques you’ve implemented. This ensures to provide a complete picture to the interviewer.

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48. Why are you applying for the Data Engineer role in our company?

Here, the interviewer is trying to see how well you can convince them regarding your proficiency in the subject, handling all the concepts needed to bring in large amounts of data, work with it, and help build a pipeline. It is always an added advantage to know the job description in detail, along with the compensation and the details of the company, thereby, obtaining a complete understanding of what tools, software packages, and technologies are required to work in the role.

49. What is your plan after joining this Data Engineer role?

While answering this question, make sure to keep your explanation concise on how you would bring about a plan that works with the company set up and how you would implement the plan, ensuring that it works by first understanding the data infrastructure setup of the company, and you would also talk about how it can be made better or further improvised in the coming days with further iterations.

50. Do you have prior experience working with Data Modeling?

If you are interviewed for an intermediate-level role, this is a question that you will always be asked. Begin your answer with a simple yes or no. It is alright if you have not worked with data modeling before, but make sure to explain whatever you know about data modeling in a concise and structured manner. It would be advantageous if you have used tools like Pentaho or Informatica for this purpose.

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