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Top 30+ Most Asked Data Engineer Interview Questions and Answers

1) What do you understand by the term data engineering?

The term data engineering is used in big data and mainly focuses on data collection and research applications. The data generated from various sources are just raw data. Data engineering is the field of study and research about data generated from various sources and converting this raw data into useful information.

In other words, data engineering is the field of study of raw data and an act of transforming, cleansing, profiling, and aggregating large data sets into useful information.

2) Why did you choose a career in data engineering? / Why do you want to make a career in data engineering?

This is a must-asked question in a data engineering interview. By asking this question, the interviewer wants to know about your understanding of the data engineering field, your motivation, and your interest in choosing the data engineering field as a career. Every organization wants to employ professionals who are passionate about their career fields.

If you are an experienced candidate, you can specify your skills and knowledge about the field and what challenges you overcame to get knowledge or any experience in the data engineering field. While answering this question, you can share your passion for data engineering, which makes you get through your challenges every day. You can also share your story and insights you have gained to impress the interviewer about what excites you most about being a data engineer.

3) What is the main role of a data engineer?

The main role of a data engineer is to set up and maintain an infrastructure that supports the data modeling and information-related applications. Data engineers have to deal with many responsibilities regarding getting the raw data and retrieving useful information from them.

As the IT field grows day by day, we need professionals to maintain the big data architecture. The data engineers are the professionals who understand data, data ingestion, extraction, transformation, data loading, and more. They are more data specific and different from the core IT professionals. They are also different from data scientists as the latter are dedicated to handling data mining, identifying patterns in data, recommending data-backed changes to the business leadership, etc. So, we can say that data engineers are a crucial link between core IT professionals and data scientists.

4) What do you understand by data modeling?

Data modeling is the process or method of documenting complex software design as a diagram so that anyone can easily understand it. It creates a simplified diagram of a software system and its data elements by using text and symbols to represent the data and how it flows. It provides a blueprint for designing a new database. It uses several formal techniques to create a data model for an information system.

In other words, we can say that data modeling is a conceptual representation of data objects that are associated between various data objects and the rules.

5) Do you have any experience with data modeling?

This question is mainly asked the candidates who have some experience in data modeling. Even if you don't have any experience in data modeling, it would be best if at least you define it as data modeling is a process of transforming and processing fetched data into useful information and then sending it to the right individuals. The interviewer asks this question to check whether your experience is satisfactory for this position or not. You can explain accordingly.

If you are an experienced candidate, you can explain what you've done specifically in your previous job. You can explain the tools like Talend, Pentaho, or Informatica that are mainly used in data modeling. If you don't know about these tools, you can tell about the tasks and tools you are aware of.

6) What are the core skills required in a data engineer?

A data engineer must know the following technical and soft skills to perform their responsibilities efficiently and effectively:

  • Good knowledge of coding. They must have a basic understanding of some programming languages like C/C++, Java, Python, Golang, Ruby, Perl, Scala, etc.
  • Knowledge of operating systems such as macOS, Microsoft Windows, Linux, Solaris, UNIX, etc.
  • A good understanding of database design and architecture. They must also be proficient in both SQL and NoSQL databases systems.
  • Some experience in data stores and distributed systems like Big Data and Hadoop.
  • They must have expertise in Data Warehousing and ETL tools.
  • They must have critical thinking skills to evaluate issues and then develop solutions.
  • Some machine learning skills and knowledge of cloud computing tools are also required.

7) As a Data Engineer, how can you handle a job-related crisis?

The main motive behind asking this question, the interviewers want to know your mindset regarding your job. Data engineers have a lot of responsibilities, so it is very much obvious that you will face challenges in your job. While answering this question, you should be honest and answer them that what you did to solve these types of problems in your previous job. By asking this question, the interviewer also knows if you have yet to face any urgent issue in your job or if this is your first data engineering role. You can also take a hypothetical situation to tell your interviewer what you would do if you get any such situation. For example, you can say that if data were to get lost or corrupted, you would work with the IT department to ensure that data backups were ready to be loaded and other team members get access to what they required.

8) What do you know about our business? Why should we hire you?

You can face this interview question in any job interview. This is a fundamental question, and you have to answer this question positively. While answering this question, you can point out some exciting features of the role and the job involved. You should research well about the company before going to the interview. You can state what kind of work the company is doing in that field that attracts and motivates you to join the company. Here, you can also specify and highlight your qualification, experience, skills, and personality to show how your experience would benefit the company and what you have gained will help you be a better Data Engineer.

9) What is the difference between structured and unstructured data?

Following is the list of the key differences between structured and unstructured data:

Structured Data Unstructured Data
Structured data is a type of quantitative data. It usually contains numbers and values. Unstructured data is a type of qualitative data. It usually contains audio, video, sensors, descriptions, etc.
Structured data is stored in tabular formats like excel sheets or SQL databases. Unstructured data is stored as audio files, videos files, or NoSQL databases.
Structured data requires less storage space and is highly scalable. Unstructured data requires huge storage space and is also difficult to scale..
Structured data is mainly used in machine learning and to derive machine learning algorithms. Unstructured data is mainly used in natural language processing and text mining.
Structured data has a pre-defined data model. Unstructured data does not have a pre-defined data model.
The main structured data sources are online forms, GPS sensors, network logs, web server logs, OLTP systems, etc. The main source of unstructured data is email messages, word-processing documents, pdf files, etc.
Examples of structured data are names, addresses, dates, credit card numbers, stock information, geolocation, etc. Examples of unstructured data are audio, video, image files, log files, sensors, or social media posts.

10) What do you understand by *args and **kwargs in data engineering?

This is an advanced-level data engineering question. This is a complex coding question, and these types of coding questions are commonly asked in data engineering interviews.

Both *args and **kwargs are used in Python and specify that a function accepts any number of arguments. The *args is used to define an ordered function, and **kwargs represents an unordered argument used in a function. Here, *args refer to regular arguments (e.g. myFun(1,2,3)) and **kwargs refer to keyword arguments (e.g. myFun(name="jtp")).

For example, if you want to declare a function for summing numbers. You will get one problem with this function by default, as it only accepts a fixed number of arguments.


Following is a regular function for summing numbers, expecting a single argument of type list:

We can use it to find the sum:

Here, the *args facilitates you to do the same without using a list:

On the other hand, **kwargs are used to unpack dictionaries. For example, if you want to multiply 3 numbers, those come from some external source and are stored as key-value pairs. Here, the keys are always identical, but the values change.

Following is the simple backend code:

A simple approach:

By using **kwargs:

11) What is the difference between a Data Warehouse and an Operational Database?

Operational databases are used to insert, update and delete SQL statements. They mainly focus on speed and efficiency. That's why analyzing data is a little more complicated in operational databases. On the other hand, data warehouses mainly focus on aggregations, calculations, and select statements. These functions make data warehouses an ideal choice for data analysis.

12) What are the different design schemas used in data modeling?

There are mainly two design schemas used in data modeling. They are as follows:

1. Star schema: Star schema is the basic and simplest schema among the data mart schema. It follows a mature modeling approach widely adopted by relational data warehouses. It needs modelers to classify their model tables as either dimension or fact. Dimension tables specify business entities, i.e., the things we model, and the business entities consist of products, people, places, and concepts, including time itself.

The star schema is mainly used to develop or build a data warehouse and dimensional data marts. It is also a necessary cause of the snowflake schema. The star schema is also efficient in handling basic queries.

2. Snowflake schema: The snowflake schema is a typical variant of the star schema. It is a logical arrangement of tables in a multidimensional database in such a way that the entity-relationship diagram resembles a snowflake shape. That's why it is called snowflake schema. The snowflake schema is represented by centralized fact tables connected to multiple dimensions. In the snowflake schema, the centralized fact table is connected to multiple dimensions, and all dimensions are present in a normalized form in multiple related tables.

The snowflake structure occurs in the case when the dimensions of a star schema are detailed and highly structured, having several levels of relationship, and the child tables have multiple parent tables. This schema affects only the dimension tables and does not affect the fact tables.

13) What are the advantages and disadvantages of the Star schema?

Following is the list of key advantages and disadvantages of the Star schema:

Advantages of Star Schema:

  • It makes queries simple: Star schema uses very simple join logic in comparison to other join logic, which requires fetching data from a transactional schema that is highly normalized.
  • It simplifies business reporting logic: Transactional schema is highly normalized compared to the star schema. So, the star schema clarifies common business reporting logic, such as as-of reporting and period-over-period.
  • No need to design cubes: All OLAP systems use star schema to design OLAP cubes efficiently. Most OLAP systems deliver a ROLAP mode of operation using a star schema as a source without designing a cube structure.

Disadvantages of Star schema:

  • Star schema is a highly de-normalized schema state so that that data integrity may be a drawback. In this schema, data integrity is not enforced well.
  • It is not a highly normalized data model, so it is not flexible if the analytical needs a normalized data model.
  • The star schema doesn't reinforce many-to-many relationships within business entities frequently.

14) What are the main components of a Hadoop application?

Following is a list of the main components of a Hadoop application:

  • HDFS: HDFS stands for Hadoop Distributed File System. It is the primary data storage system used by Hadoop applications. This file system stores the Hadoop data. It uses NameNode and DataNode architecture to implement a distributed file system that provides high bandwidth, high-performance access to data across highly scalable Hadoop clusters.
  • Hadoop Common: This is a common set of utilities and libraries utilized by Hadoop.
  • Hadoop YARN: This is mainly used for resource management within the Hadoop cluster. We can also use it for task scheduling for users.
  • Hadoop MapReduce: It is used for large-scale data processing.

15) What are the advantages and disadvantages of the Snowflake schema?

Following is the list of key advantages and disadvantages of the Snowflake schema:

Advantages of the Snowflake schema

  • Snowflake schema provides better data quality. In this schema, data is more structured, so data integrity problems are reduced and not very common.
  • Snowflake schema uses less disk space than the denormalized model because data is normalized in this model, and there is minimal data redundancy.
  • There is a very low risk of data integrity violations and a low level of data redundancy in the Snowflake schema model. That's why maintenance is simple and easy.
  • In the Snowflake schema, we can use complex queries that don't work with a star schema. It provides more space for powerful analytics.
  • The Snowflake schema supports many-to-many relationships.

Disadvantages of the Snowflake schema

  • The Snowflake schema is harder to design as compared to the Star schema.
  • The Snowflake schema requires more complex queries that use several numbers of joins that can significantly decrease the Snowflake schema's performance.
  • Maintenance in the Snowflake schema is also more complex because of the many different tables present in the data warehouse.
  • Queries in the Snowflake schema can be very complex and may have many levels of joins between many tables.
  • Because of the many joins that have to produce the final output, queries in the Snowflake schema are slower in some cases.
  • We require some specific skills to work with data stored using snowflake schema.

16) What are the key differences between Star schema and Snowflake schema?

Following is the list of key differences between Star schema and Snowflake schema:

Star Schema Snowflake Schema
In Star schema, the dimension table contains the hierarchies for the dimensions. In the Snowflake schema, there are separate tables for hierarchies.
In this schema, the dimension tables cover a fact table. In this schema, the dimension tables cover a fact table, and then they are further covered by dimension tables.
In this schema, the fact table and dimension table are connected by a single join. In this schema, many joins are used to fetch the data.
It has a simple DB design. It has a complex DB design.
It can work fine even with denormalized queries and data structures. It works well only with the normalized data structure.
In this schema, a single dimension contains the aggregated data. Here, data is split into different dimension tables.
Data redundancy is high in Star schema. Data redundancy is very low in the Snowflake schema.
It provides faster cube processing. Due to complex joins, cube processing is slow in the Snowflake schema.

17) What are the four V's of big data?

The four V's of big data are:

  1. Velocity: The first V is Velocity, which specifies how big data is being generated over time. It is used to analyze the data.
  2. Variety: The second V is Variety which specifies the various forms of big data, such as images, log files, media files, and voice recordings.
  3. Volume: The third V specifies the Volume of the data. For example, the number of users, the number of tables, the size of data, or the number of records within the table.
  4. Veracity: The fourth V specifies the Veracity related to the uncertainty or certainty of the data. It is used to decide the accuracy of the data.

18) What are the key differences between OLTP and OLAP?

Following is the list of key differences between OLTP and OLAP:

OLTP stands for Online Transaction Processing. In this type of data processing, executing several transactions occurs concurrently. For example, online banking, online shopping, sending text messages, etc. OLAP stands for Online Analytical Processing. This data processing type provides a system for performing multi-dimensional analysis at high speeds on large volumes of data. This mainly works on a data warehouse, data mart, or other centralized data stores.
OLTP is mainly used to manage operational data. OLAP is mainly used to manage informational data.
It is mainly used by clients, clerks, and IT professionals. It is mainly used by analysts, executives, managers, and other skilled workers.
OLTP is customer-oriented. OLAP is market-oriented.
It comes with comparatively smaller database size. It comes with huge database size.
It manages extremely detailed current data and is mainly used for decision-making. It is used to manage a huge amount of historical data. It facilitates aggregation and summarization and manages and stores data at different levels of granularity, so the data becomes more comfortable to be used in decision-making.
It follows an entity-relationship data model along with an application-oriented database design. It follows either a snowflake or a star model and a subject-oriented database design.
It is completely normalized. It is partially normalized.
It works with small data size. It works with a huge volume of data.
It provides an access mode for Read/Write operations. It provides the access mode for mostly Write.
The processing speed is very fast. Its processing speed varies accordingly and depends on the complex queries and number of files it contains.

19) What do you understand by NameNode in HDFS?

NameNode is one of the most important parts of HDFS. It is the master node in the Apache Hadoop HDFS Architecture and is used to maintain and manage the blocks present on the DataNodes (slave nodes).

NameNode is used to store all the HDFS data, and at the same time, it keeps track of the files in all clusters as well. It is a highly available server that manages the File System Namespace and also controls access to files by clients. Here, we must know that the data is stored in the DataNodes and not in the NameNodes.

20) What are DataNodes in HDFS?

Contrary to the NameNodes, DataNodes are the slave nodes in HDFS. The actual data is stored on the DataNodes. A functional file system can have more than one DataNode, with data replicated across them. DataNodes are mainly responsible for serving, read and write requests for the clients. On startup, a DataNode connects to the NameNode, spinning until that service comes up.

21) What are the key differences between NameNode and DataNode in Hadoop?

Following is the list of key differences between NameNode and DataNode in Hadoop:

NameNode DataNode
NameNodes are the centerpiece of HDFS. They are used to control and manage the HDFC. They are known as the Master in the Hadoop cluster. DataNodes are used to store the actual business data in HDFS. They are also known as the Slave in the Hadoop cluster.
NameNode only stores the metadata of actual data. It acts as the directory tree of all files in the file system and tracks them across the cluster. For example, filename, path, no. of data blocks, block IDs, block location, no. of replicas, slave-related configuration, etc. DataNode acts as the actual worker node where Read/Write/Data processing is handled.
NameNode is responsible for constructing the file from blocks as it knows the list of the Blocks and their location for any given file in HDFS. DataNode makes a constant communication with NameNode to do the job.
NameNode plays a critical role in HDFS; when the NameNode is down, the HDFS/Hadoop cluster cannot be accessed and is considered down. DataNode is not so important as when it is down. It does not affect the availability of the data or the cluster. NameNode will arrange replication for the blocks managed by the DataNode that is not available.
NameNode is generally configured with a lot of memory (RAM) because the block locations are held in the main memory. DataNode is generally configured with a lot of hard disk space because the actual data is stored in the DataNode.

22) What do you understand by Block and Block Scanner in HDFS?

In HDFS, Blocks are the smallest unit of a data file. When Hadoop gets a large file, it automatically slices the file into smaller chunks called blocks. On the other hand, Block Scanners are used to verify the list of blocks presented on a DataNode, is put successfully or not.

23) What is Hadoop streaming?

Hadoop streaming is one of the most used utilities provided by Hadoop. It comes with the Hadoop distribution. This utility facilitates us to create maps and perform reduction operations easily. It can also be submitted into a specific cluster for usage.

24) What are some important features of Hadoop?

Following is a list of some Hadoop features that make Hadoop popular in the industry, more reliable to use, and the most powerful Big Data tool:

  • Hadoop is an open-source and free-to-use framework. It is an open-source project, so the source code is available online for anyone. Anyone can understand and use it and make some modifications according to their industry requirement.
  • Hadoop is fault-tolerant. If somehow any of your systems got crashed, it provides data availability. In Hadoop, data is replicated on various DataNodes in a Hadoop cluster that ensures data availability every time. By default, Hadoop makes 3 copies of each file block and stores it in different nodes.
  • Hadoop provides parallel computing, which ensures faster data processing.
  • In Hadoop, data is stored in separate clusters away from the operations.
  • Hadoop provides high availability of data. Its fault tolerance feature provides high availability in the Hadoop cluster. If any DataNode goes down, you can retrieve the same data from any other node where the data is replicated.
  • Hadoop provides the data redundancy feature. It is used to ensure no data loss.
  • Hadoop is cost-effective compared to other traditional relational databases that require expensive hardware and high-end processors to work with Big Data.
  • Hadoop provides flexibility as it is designed in such a way that it can deal with any kind of dataset like structured (MySQL Data), Semi-Structured (XML, JSON), Unstructured (Images and Videos) efficiently.

25) What happens when Block Scanner detects a corrupted data block? How does it handle this?

When a Block Scanner detects a corrupted data block, it follows the following steps:

  • After Block Scanner detects a corrupted data block, the DataNode reports to the NameNode.
  • After that, NameNode starts creating a new replica using the original corrupted block.
  • Finally, the replication counts the correct replicas and matches it with the replication factor. If the match is found, the corrupted data block will not be deleted.

26) How does the NameNode communicate with the DataNode? / What are the two messages that NameNode gets from DataNode?

The NameNode communicates with the DataNode via messages. There are two messages that are sent across the channel:

  1. Block reports
  2. Heartbeats

27) What steps should we follow to achieve security in Hadoop?

We should perform the following steps to achieve security in Hadoop:

  • The first step we should follow is to secure the authentication channel of the client to the server and provide time-stamped to the client.
  • After getting the time-stamped, the client uses the received time-stamped to request TGS for a service ticket.
  • In the last step, the client uses a service ticket for self-authentication to a specific server.

28) What is Combiner in Hadoop?

Combiner is a function in Hadoop that plays an important role in reducing network congestion. The Hadoop framework provides this function. This function is an optional step between Map and Reduce. It is mainly used to process the output data from the Mapper before passing it to Reducer. It takes the output from the Map function, creates key-value pairs, and then submits it to the Hadoop Reducer. It summarizes the final result from Map into summary records with an identical key. It runs after the Mapper and before the Reducer.

29) What do you understand by Heartbeat in Hadoop?

In Hadoop, Heartbeat is a message used to communicate between NameNode and DataNode. DataNode sends a signal to NameNode in the form of Heartbeats. DataNode sends it to NameNode regularly to show its presence.

30) What do you understand by data locality in Hadoop?

We all know that in a Big Data system, the size of data is huge, so it does not make sense to move data across the network for computation. In Hadoop, data locality is the process of moving the computation close to the node where the actual data resides instead of moving large data to computation. This process minimizes the network congestion and increases the overall computation throughput of the system. This process is called data locality because the data remains local to the stored location.

31) What is the full form of FSCK in HDFS?

In HDFS, FSCK is an acronym that stands for File System Check. It is one of the most important commands used in HDFS. It is mainly used when we have to check for problems and discrepancies in files.

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

Following is the list of key differences between NAS and DAS in Hadoop:

NAS is an acronym that stands for Network Attached Storage. DAS is also an acronym that stands for Direct Attached Storage.
In NAS, computation and storage layers are separate layers, and storage is distributed over different servers on a network. That's why it provides high storage capacity. In DAS, storage is not distributed, and it is attached to the node where computation takes place. That's why it provides lower storage capacity.
Storage capacity in NAS is 109 to 1012 bytes. Storage capacity in DAS is 109 bytes.
Apache Hadoop follows the principle of moving processing near the location of data. So it requires a local storage disk for computation. DAS provides very good performance on a Hadoop cluster. It can also be implemented on commodity hardware, which is more cost-effective than NAS.
NAS storage is preferred only when we have very high bandwidth. DAS can be used with any bandwidth.
In NAS, the data transmission medium is Ethernet or TCP/IP. In DAS, the medium of data transmission is IDE/SCSI.
In NAS, the management cost per GB is moderate. In DAS, the management cost per GB is high.

33) What do you understand by FIFO scheduling?

FIFO scheduling is a job scheduling algorithm of Hadoop. As the name suggests, FIFO stands for First In First Out. So, in FIFO scheduling, the tasks or applications that come first are served first. This is the default scheduling used in Hadoop.

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