30+ Data Engineer Interview Questions & Answers

If you are looking for data engineer interview questions and answers, look no further. I've collected a list of questions to help you start as a study guide before you begin your employment path. Data Engineers are a vital part of the technology organization. They are playing a huge role in helping to understand customers and acquire information about the business that can be vital towards making decisions. Data Engineers are required to have a strong knowledge of programming as they sometimes write SQL, R and much more. They are constantly trying to dig up information on what the business is doing well and what it could be doing better. They play a significant role in today's startup atmosphere. With all interview question answers, they are mock answers. So please spend the time to write down your own answers to these questions and practice your overall delivery of them. That will ensure you are executing the right level of confidence in your answers thus giving you a better chance of overall employment.

Data Engineer Interview Questions Table of Contents


Data Engineer Interview Questions & Answers

1. What is data engineering to you?

Data engineering refers to an employment role, which is known within the field of big data. It references data architecture or infrastructure. The data is generated by many varieties of sources. From internal databases to external data sets. The data has to be transformed, profiled and cleansed for the purpose of business needs. This raw data is also termed as Dark Data. The practice of working with the data and making it accessible to the employees who need it to be informed about their decisions is called a Data Engineer.

2. Discuss the four V's of Big data.

They include the following:

  • Velocity: Analysis of the streaming data. Usually, Big Data keeps getting generated over the course of time. Velocity refers to the rate at which the data is being generated.
  • Variety: this point is about the different forms of Big Data. It can be within log files, voice recordings, images, and media files.
  • Volume: the scale of data. The scale may be defined within the term of the number of users, size of data, number of tables and the number of records.
  • Veracity: this concerning the certainty or the uncertainty of the data. How sure would you be concerning the accuracy of the data.

3. What is the difference between unstructured and structured data?

The structured data is that which can be stored within a traditional database system and these may include MS Access, SQL Server and Oracle. It is stored within rows and different columns. A lot of the online application transactions are Structured Data. The structured data is that which can be easily defined according to the data model. The Unstructured data, though cannot be stored in terms of the rows and columns. Therefore, it cannot be stored within a traditional database system. Usually, it has a varying size and content. The examples when it comes to unstructured data would include tweets, Facebook likes and the Google search items. Some of the Internet of Things data happens to be the unstructured forms of data. It is hard to define the unstructured data within a data model, which is defined. Some of the software, which supports the unstructured data include such things as MongoDB and Hadoop.

4. Describe a data disaster recovery situation and your particular role.

You are required to complete daily tasks assigned by your peers. Hiring managers are seeking Data Engineers that are able to contribute to emergency situations as well as contribute to the overall success of the product decision making. When the data is not accessible then it may have damaging effects on the operations of the company. The companies need to make certain they are ready with the appropriate resources to deal with failure if it happens. A lot of the time, it becomes an all hands on deck circumstances.

5. Explain the responsibilities of a data engineer.

The task of the data engineer is to handle the data stewardship within the company.

They also handle and maintain the source systems of the data and the staging areas.

They simplify the data cleansing and improvement of data re duplication and subsequent building.

They provide and execute ELT and data transformation.

They do ad-hoc data queries building as well as, extraction.

6. What are the main forms of design schemas when it comes to data modeling?

There are two types of schemas when it comes to data modeling:

  • Star Schema: this type of schema is divided into two. One would be the fact table and the other is the dimension table where the dimension tables are connected to the fact one. The foreign key when it comes to the fact table is in reference to the primary keys that are present within the dimension tables.
  • The snowflake schema is the other one where the levels of normalization are increased. In this case, the fact table would be the same as of the star schema. Because of the different layers of the dimension tables, it looks like a snowflake and so hence the name.

7. Do you have any experience when it comes to data modeling?

You may say that you have worked on a project for a health/ insurance or health client where they have utilized the ETL tools including the Informatica, Talend and even Pentaho. This would be for the purposes of transforming and processing the data as fetched from the MySQL/RDS/SQL Database and sends the information to the vendors that would assist with the increase of the revenues. One might illustrate below the high-level architecture of the data model. it entails a primary key, attributes and relationship constraints etc.

8. What is one of the hardest aspects of being a data engineer?

You may want to avoid an indirect answer to this particular question because of the fear of highlighting a weakness you may have. Understand that this is one of those questions that doesn't have a perfect desired outcome. Instead, try and identify something which you may have had a hard time with and the way that you dealt with it.

9. Illustrate a moment when you found a new use for existing data and it had a positive effect on the company.

As a data engineer, I will most likely have a better perspective or understanding of the data within the company. If certain departments are looking to garnish a set of insight from within a product, sales or marketing effort I can help them to better understand it. To add the biggest value to the strategies of the company it would be valuable to know the initiatives of each department. That would allow me, the Data Engineer a greater chance of providing valuable insight from within the data.

10. What are the fields or languages that you need to learn in order to become a data engineer?

  • Mathematics such as probability and linear algebra
  • Statistics like regression and trend analysis
  • R and SAS learning techniques
  • SQL databases, Hive QL
  • Python
  • Machine learning techniques

11. What are some of the common issues that are faced by the data engineer?

  • Real time integration and continuous integration
  • Storing a large amount of data would be one thing and the information from that data is also an issue
  • Considerations of the processors and the RAM configurations
  • The ways to deal with failure and asking whether there is fault tolerance there or not
  • Consideration of the tools, which can be used, and which of them will provide the best storage, efficiency, performance and results.

12. What are all the components of a Hadoop Application?

Over time, there are different ways in which the Hadoop application would be defined. Though in a lot of the cases, there are four core components relating to the Hadoop application. These include the following:

  • Hadoop Common: this is common set of libraries and utilities that have been utilized by Hadoop.
  • HDFS: this relates to the file system in which the Hadoop data is stored. It is one of the distributed file systems that have a high bandwidth.
  • Hadoop MapReduce: this is based according to the algorithm for the provision of large-scale data processing.
  • Hadoop YARN: this can be used for the purpose of resource management within the Hadoop cluster. It may also be used or the scheduling of tasks for the users.

13. What is the main concept behind the Apache Hadoop Framework?

The Apache Hadoop is based according to the concept, which is oriented toward Mapreduce. When it comes to this algorithm, Map and Reduce type of operations are the ones, which are used for processing the large data sets. The Map method is the one which does the filtering and sorting of the particular data the Reduce method also performs summaries of the data. The main points within this point would include fault tolerance as well as scalability. When it comes to Apache Hadoop, these particular features can be achieved by multi-threading and the efficient implementation of Map Reduce.

14. What is the main difference that is between NameNode Backup Node and Checkpointnode when it comes to HDFS?

  • NameNode: at the core of the HDFS file system which manages the metadata. That is to say, the data of the filing system is not stored on the NameNode but that it has the directory tree of the files, which are all present on the HDFS system on a Hadoop type cluster. There are two files, which are used for the sake of the namespace.
  • Edits file: this is a log of changes, which are made to the namespace since checkpoint.
  • Fsimage file: a file that will keep track of the latest checkpoint within the namespace.
  • BackupNode: this particular node provides check pointing functionality but it also monitors the up to date in memory copy for the filing system namespace which is synchronized with the active NameNode.
  • Checkpoint Node: this is what keeps track of the latest checkpoint within a directory that has a similar structuring as compared to the one of the NameNode directory. The checkpoint node allows for checkpoints for the particular namespace at regular intervals through the downloading of the edits and the fsimage files from the NameNode and merging it locally. The new image would be updated back to the active NameNode.

15. Explain how the analysis of Big data is helpful when it comes to the increase of business revenue?

Big Data has gained a significance for a variety of businesses. It assists them to differentiate from others and by doing increases the odds of revenue gain. Through the predictive analytics, big data type analytics allows businesses to customize the suggestions and recommendations. Similarly, the big data type analytics allows the enterprises to launch their new products as depending on the needs of the client and their preferences. These factors are those, which make the businesses able to earn more revenue, and so companies are frequently using big data type of analytics. The corporations may then encounter a rise of 5 to 20 percent in revenue through the implementation of the big data type of analytics. Some of the popular technology companies or organizations are those that are using big data analytics in order to increase their revenue. They include technology companies like Facebook, Twitter and even Bank of America.

16. What are the steps to be followed for deploying a Big Data solution.

  • Data ingestion: the first step for the deployment of a big data solution would be data ingestion. That is to mention the extraction of data from different sources. The data sources could be from a CRM like SAP, Salesforce, Highrise or RDBMS such as MySQL or log files, internal databased and much more. The data can then be ingested through batch jobs or real time streaming. The extracted data would then be stored within HDFS.
  • Data Storage: after the data ingestion, the next step would be to store the extracted data. The data would either be stored in a HDFS or NoSQL database preferably. The HDFS storage would wok especially well for the sequential access though HBase when it comes to random read or write access.
  • Data processing: the concluding step when it comes to the deployment of a big data solution would be the data processing. The data is then processed through one of the main frameworks for processing such as MapReduce or Pig.

17. What are some of the important features inside Hadoop?

Hadoop supports the processing and storage of big data. It represents the best solution when it comes to the handling of the big data type challenges. Some of the main features when it comes to Hadoop would include the following:

  • Open Source: Hadoop represents an open source type framework which is to say that it is available free of charge. The users may also be allowed to alter the source code according to the requirements.
  • Distributed processing: Hadoop supports the distributed processing of data whereby there is faster processing. The data within Hadoop HDFS is stored in a distributed fashion and MapReduce is obligated to the parallel processing of the that data.
  • Fault tolerance: Hadoop itself is highly tolerant. It allows for the creation of three replicas for each of the blocks at different nodes by default. This number may be altered depending on the particular requirements. Therefore, it is possible to change this according to the requirements. It is possible to recover the data from another node in the event that one of the nodes is not successful. The particular detection of the node failure and the recovery of data is done automatically.
  • Scalability: the other significant feature of Hadoop would is scalability. It is compatible with the many types of hardware and it's easy to access that new hardware within particular infrastructure nodes.
  • Reliability: Hadoop stores the data in the cluster in a reliable manner, which would be independent of all other operations. That means the data that is stored within the Hadoop environment is not be affected by the failings of the machine.
  • High availability: the data is stored within Hadoop and becomes available to access even after the hardware failure. In the event of hardware failure, the data may even be accessed from other paths.

18. What is the difference between an architect for data and a data engineer?

The data architect is that person that manages the data particularly when one is dealing with different numbers for a variety of data sources. A person may have an in-depth knowledge of the way the database works, how the data is connected to business problems and the way the changes would disturb the organization’s usage and then a data architect would manipulate the data architecture according to them. the main responsibility of the data architect would be working on data warehousing, development of the data architecture or the enterprise data warehouse/ hub. The data engineer would assist with the installation of data warehousing solutions, data modeling, development and the testing of database architecture.

19. Describe one time when you found a new use case for the present database, which had a positive effect on the enterprise.

During the era of Big Data having SQL would lack the following features:

  • RDBMS are the schema oriented DB so it is structured better for the structured data and not for the semi structured or the unstructured data.
  • Not being able to process the unpredictable or the unstructured data.
  • It is not scalable horizontally, which is to say the parallel execution and storing is not possible within SQL.
  • It suffers from performance issue once the number of users starts to increase.
  • Use case is mainly utilized for Online transactional processing.
In order to overcome these particular setbacks, you can utilize NoSQL DB. That is to say not only SQL. In so doing within the project, it is possible to use different types of NoSQL DB such a Mongo DB, Graph DB and HBase.

20. Give a healthy description of what Hadoop Streaming is to you.

Hadoop distribution gives Java utility known as Hadoop streaming. With the use of Hadoop Streaming, it possible to create and run the Map Reduce tasks with a script, which is executable. It is possible to create the executable scripts for the Mapper as well as the Reducer functions. These executable scripts are passed to Hadoop Streaming in a command. The Hadoop Streaming utility allows for the creation of Map and Reduce jobs and submits them to a particular cluster. It is also possible to monitor these jobs with the particular utility.

21. Can you tell me waht the Block and Block Scanner in HDFS.

A large file when it comes to HDFS is broken into different parts and each of them is stored on a different Block. By default, a Block has a 64 MB capacity within HDFS. Block Scanner refers to a program which every Data node in HDFS runs periodically in order to verify the checksum of each block stored within the data node. The goal of the Block Scanner would be detecting the data corruption errors on the Data node.

22. What are the different means that Hadoop is run.

  • Standalone Mode: by default, Hadoop runs in a local mode whereby it is on a non-distributed and single node. This standalone mode utilizes a local filing system in order to perform both input and output operations with efficiency. That particular mode would not support the usage of the HDFS so it can be used for the sake of debugging. There is no custom configuration, which is required for the configuration files within the particular mode.
  • Pseudo Distributed Mode: in this mode, Hadoop is run on a single node in the same way as the Standalone mode. Within this mode, each daemon runs in a separate Java process. As the daemons run on a single node, there is the same node for the Master and Slave nodes.
  • Fully distributed mode: in the fully distributed mode, the daemons are running on separate individual modes and so to form a multi-node cluster. There are different nodes for both the Master and Slave nodes.

23. How would you achieve security within Hadoop?

Kerberos is a tool utilized often to achieve security within Hadoop. There are 3 steps that would allow for the access of a service while using Kerberos. Each step is part of a message exchange with a server.

  • Authentication: the first step would be to secure the authentication of the client to the server and then providing a time-stamped TGT to the client.
  • Authorization: for this step, the client would use the received TGT in order to request a service ticket from the ticket-granting server.
  • Service request: this is the final step in order to obtain some security within Hadoop. Then the client would utilize the service ticket for authenticating himself to the particular server.

24. What is data, which is happened to be stored within a HDFS NameNode?

The NameNode refers to the central node of a HDFS system. It does not store any of the data from the Map-Reduce operations. Though, it has metadata, which has been stored within the HDFS DataNodes. NameNode has the directory tree for the files within the HDFS filesystem. With the use of this metadata, it ends up managing the data, which is stored in the different DataNodes.

25. What may occur if NameNode crashes in the HDFS cluster?

There is a NameNode when it comes to the HDFS cluster. This particular node maintains the metadata concerning the DataNodes. Because there is usually only one NameNode, it would be the single point of failure for the HDFS cluster. When the NameNode comes to an end, the system may not be available. You may specify a secondary NameNode within the HDFS Cluster. The secondary NameNametakes the regular checkpoints of the filing system within HDFS. However, it is not the backup for NameNode. It can be used for recreating NameNode and restarting it in the event of a crash.

26. Which are the two messages, which NameNode gets from DataNode within Hadoop?

There are two messages which are attained from every DataNode:

  • Block Report: this is a listing of the data blocks hosted on the DataNode. The report may also be useful for the functioning of the NameNode. With this particular report, NameNode would get information about the data that is stored on a particular DataNode.
  • Heartbeat: this message signals that DataNode is still alive. The periodic receipt of Heartbeat is quite significant for NameNode in order to ascertain whether or not to use NameNode or not.

27. How does Indexing work when it comes to Hadoop.

Indexing in Hadoop works in two different levels:

  • Index based on the File URL: in this scenario, the data may be indexed based according to the different files. When you search for data, the index is going to return the files that have the data.
  • Index based according to InputSplit: In this scenario, the data may be indexed according to the locations where the input split is located.

28. How would you optimize algorithms or structured code in order to make them run more efficiently?

The answer should be yes. The performance matters and it does not depend on the data being used for the particular project. This is a question of which the person providing you the interview may be looking for you to share some of your prior experience. If you have done some optimizing of algorithms or code, definitely bring that up. For the beginner, it would be best if you showed personal projects that you may have executed in the past which do such a task. It's better in this situation to be honest about your prior work but also show some level of excitement and enthusiasm for gaining more experience in this particular category for the data engineer role.

29. How do you approach data preparation as a data engineer?

Data preparation refers to a crucial step when it comes to the big data projects. A big data type of interview could mean one question being based off of data preparation. The person who is interviewing you is trying to understand part of your process for this. It is best to try and come prepared with what your 'data preparation' steps are. Data preparation is required in order to get the necessary data that can be used for the sake of modeling. This message needs to be conveyed to the interviewer. There should also be emphasis on the type of model, which should be used and the reasons behind choosing the model. then finally, there should be a need to discuss the crucial data preparation terms such as the transformation of variables, unstructured data and identifying gaps.

30. What are the types of configurations (or configuration files) when it comes to Hadoop?

  • Core-site.xml: Hadoop core configuration settings such as the I/O settings, which are common for HDFS and MapReduce. It may use a hostname for a port.
  • Mapred-site.xml: would specify a framework name for MapReduce through setting mapreduce.framework. name.
  • Hdfs-site.xml: configuration file has HDFS daemons settings. There is also specification of the default block permission and replication checking on HDFS.
  • Yarn-site.xml: configuration file specifies configuration settings for ResourceManager and NodeManager.

31. How do you restart your daemons within Hadoop?

In order to restart daemons you must first stop all daemon operations. Everything must be at a stall before you begin. The Hadoop directory has the sbin directory, which stores the script files in order to stop and start the daemons within Hadoop. You can use or access the daemons command /sbin/stop-all.sh in order to stop the daemons and then you can use /sin/start-all. sh command in order to start the daemons again.

32. What is the difference between NAS and DAS in Hadoop cluster?

NAS stands for Network Attached Storage and DAS stands for Direct Attached Storage.

  • In NAS compute and storage, layers would be separated. Storage is then distributed over different servers on the network.
  • In DAS, the storage can be attached to the node where computation happens.
  • Apache Hadoop is based according to the principle for moving processing near the particular location of the data. Therefore, it needs the storage disk to be local to computation.
  • With DAS, you get performance on a Hadoop cluster. Similarly, DAS may be implemented on commodity hardware. Simply put is just more cost effective. Only when you have a high bandwidth, which is around 10 GbE, it would be preferable to utilize NAS storage.

33. How does inner-cluster data copying function work within Hadoop?

In Hadoop, there is a utility which is known as DistCP, or Distributed Copy and its task is to perform large intra-cluster copying of the data. this utility is based as well on MapReduce. It creates Map tasks for the files given as the input. After every copy using the Distributed Copy, it is recommended to run crosschecks in order to confirm there is no corruption and copy is complete.

Related Hiring Resources

Data Engineer Resume Example
Data Engineer Job Description
Data Engineer Cover Letter Sample
Big Data Engineer Cover Letter Sample
author: patrick algrim
About the author

Patrick Algrim is an experienced executive who has spent a number of years in Silicon Valley hiring and coaching some of the world’s most valuable technology teams. Patrick has been a source for Human Resources and career related insights for Forbes, Glassdoor, Entrepreneur, Recruiter.com, SparkHire, and many more.


Help us by spreading the word