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Top 28 Data Engineer Interview Questions & Answers for Beginners and Experienced

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11th Mar, 2020
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Top 28 Data Engineer Interview Questions & Answers for Beginners and Experienced

Preparing for an interview but don’t know how to go about it? You can start with our list of data engineer interview questions and answers. 

Data engineer interviews are one of the hardest to crack. There’s so much you should know about. But don’t worry, because our list of interview questions will help you in this regard. After you’ve gone through this list, you’d know the answers to many prominent questions a recruiter might ask. Also, this list will give you an idea of what you should study and learn while preparing for the interview. 

Let’s get started. 

Top Data Engineer Interview Questions & Answers 

Q.1 – What is Data Engineering?

Data engineering is a software engineering approach to developing and designing information systems. It focuses on the collection and analysis of data. While data scientists perform various tasks with big data, someone has to collect all of this data before, and data engineers perform that task. Data engineers are responsible for the development and maintenance of databases as well. Data engineers convert raw data into usable data. 

Q.2 – What do you understand by Data Modelling?

When you create a data model for an information system so you can track their data, it’s called data modeling. These data models become tables in a DB (database). For example, if you want to analyze your customer behaviors, every customer in your database would be a data model. It is the conceptual representation of data values associated with rules

Q.3 – What is Hadoop?

Hadoop is an open-source software collection of utilities that allow you to use a network of multiple computers for solving problems related to big data. It has various components that let you process mass amounts of data. The developer of Hadoop is the Apache foundation. Its extensive collection of utilities and components allow you to perform many powerful big data applications efficiently. 

Q.4 – What are the different components of Hadoop?

Hadoop is mainly made up of 4 components, and they are HDFS, MapReduce, YARN, and Hadoop Common. 

HDFS is the file system that stores all the data of Hadoop. It has a high bandwidth as its a distributed storage system. 

MapReduce processes large amounts of data; YARN is the resource management of Hadoop and allocates the required resources accordingly. Hadoop Common is a group of libraries and utilities you can use in Hadoop. 

Q.5 – What does HDFS stand for?

HDFS is a Hadoop component. HDFS stands for Hadoop Distributed File System. 

Q.6 – What is a NameNode?

A NameNode is a part of data storage in HDFS and tracks the different files present in clusters. NameNodes don’t store data. They store metadata of DataNodes, where HDFS stores its actual data. 

Q.7 – What’s the difference between unstructured and structured data?

Systems store unstructured data in unmanaged file structures while the storage for structured data is DBMS. Schema scaling of structured data is challenging, but it is quite easy to do with unstructured data. You’d use ELT (Extract, Transform, and Load) for structured data. On the other hand, you’d need to perform batch processing or data entry. 

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Q.8 – How many types of design schemas are present in Data Modelling? What are they?

There are two types of design schemas in data modeling, which are: Snowflake schema and Star schema. 

Q.9 – What happens when Block Scanner finds a corrupted block of data? Explain.

This is one of the popular data engineer interview questions. So be sure to prepare it before the When Block Scanner finds a corrupted block of data, DataNode reports it to NameNode. Then, the NameNode begins making a replica of the corrupt block by using one of its existing models. If the system doesn’t delete the corrupted data block, it creates as many replicas as there is the replication factor. The replication count has to match the same. 

Q.10 – Name all the XML configuration files present in Hadoop.

The XML configuration files present in Hadoop are HDFS-site, Mapred-site, Yarn-site, and Core-site. 

Q.11 – What is a Block in HDFS? What is a Block Scanner?

In Hadoop, a block is the smallest unit of data. A Block scanner is a component that checks and verifies the blocks present on a DataNode. Hadoop divides large data files into small data blocks for ease of storage. 

Q.12- What messages does a DataNode sends to the NameNode?

DataNodes send signals to NameNodes to inform them that they are working. The name of these signals is the Heartbeat. And if a DataNodes fails to send a heartbeat, NameNode determines that it has died and has stopped operating. 

Q.13 – State the central V’s of Big Data.

The four central V’s of big data are Velocity, Variety, Volume, and Veracity. 

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Q.14 – What is meant by COSHH?

COSHH stands for Classification and Optimization-based Schedule for Heterogeneous Hadoop systems. 

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Q.15 – Can you describe Star Schema?

Star schema has a structure similar to a star; that’s why it has its name. The center of the star could have a fact table with various dimension tables associated with it. Data engineers use it to query substantial data sets. 

Q.16 – What is Snowflake Schema? 

A snowflake schema is a form of Star schema. The only difference is, it has additional dimensions, and it derives its name from its snowflake-like structure. It has normalized dimension tables, due to which it has other tables. 

Q.17- What are the core methods of a Reducer in Hadoop?

There are several core methods in Reducer. The first one is setup () that configures parameters, cleanup () cleans temporary data sets, and the Reducer runs reduce () method with every reduced task. 

Q.18 – What is FSCK?

FSCK stands for File System Check. It’s a command of HDFS, and it uses this command to detect problems and inconsistencies in a file. 

Q.19 – Does Hadoop have multiple modes? If so, what are they?

Yes, Hadoop has three distinct modes. They are: Standalone mode, Fully distributed mode, and Pseudo distributed mode. 

Q.20 – What does YARN stand for?

YARN stands for Yet Another Resource Negotiator. 

Q.21 – How do you secure Hadoop?

For this purpose, you’ll first enable encryption at rest and in transit. You’ll need to use the secure versions of the protocols you use in Hadoop. You’ll allow SASL to protect the RPC data. You can enable SASL through the property. 

You’ll secure the authentication channel as well. The client can use the timestamp of the authentication channel for getting a service ticket, which then you can use for self-authentication. 

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Q.22 – Can you elaborate on HDFS (Hadoop Distributed File System)?

Hadoop is capable of working with distributed file systems such as FS, HFTP, and S3. The Google File System is the basis for HDFS, and it can run on a large cluster of small systems. 

Q.23 – What are the differences between Snowflake and Star Schema?

In Star schema, you have a higher chance of data redundancy, which is not the case with Snowflake schema. The DB design of Star schema is more straightforward than Snowflake. The complex join of Snowflake schema slows down its cube processing, which doesn’t happen with Star schema.

Q.24 – What is a Heartbeat in Hadoop?

In Hadoop, there are two kinds of nodes, NameNode and DataNode. The NameNode has the responsibility of storing the metadata of DataNodes and keep track of their status. DataNodes send signals to the NameNode to inform them that they are alive and are working. This signal is the Heartbeat. 

Q.25 – What do you understand by Big Data?

When you have humongous quantities of unstructured and structured data that you can’t process with conventional methods, it’s called big data. Big data is the field of analyzing and using highly complex data sets for gathering information. Traditional methods of data analysis don’t work well with such high quantities of complex data. In big data, data engineers have the task of analyzing raw data and convert it into usable data. 

Q.26 – What subjects and programming languages should a data engineer know?

A data engineer should know trend analysis, machine learning, SQL, Hive QL, probability, regression, and linear algebra. A data engineer could know many other subjects, but these are a must. 

Q.27 – What are the differences between DAS and NAS in Hadoop?

This is one of the most popular data engineer interview questions, so pay special attention to its answer. DAS stands for Direct Attached Storage, and NAS stands for Network Attached Storage. The storage capacity of NAS is 10^9 to 10^12 in the byte. On the other hand, DAS has a storage capacity of 10^9 bytes. The management costs of NAS are way less than DAS too. 

Q.28 – What is meant by the distance between nodes in Hadoop? How would you calculate it?

In Hadoop, the distance between two nodes is equal to the sum of the length to their closest nodes. You can use getDistance() to find the distance between two nodes in Hadoop. 

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With the help of these interview questions, we’re sure that you’ll prepare quite easily. Data engineering interviews don’t have to be stressful. Make sure that you get enough sleep before the meeting, many people get too anxious. 

And if you have any questions related to data engineering or interviews, feel free to ask us. We’d love to help you. 



Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Frequently Asked Questions (FAQs)

1What are the tasks and responsibilities of data engineers?

For data engineers, their primary responsibility is to prepare data for analytical or operational purposes. As a part of the IT industry, these engineers create data pipelines that connect data from several source systems. They combine, consolidate, and purify data before structuring it for use in analytics applications. Most analytic teams of companies consist of data engineers and data scientists who make data more accessible and maximize their company's big data environment. Engineers give data in usable formats to data scientists, who use the information to perform queries and algorithms for predictive analytics, machine learning, and data mining applications.

2What are the skills required to work as a data engineer?

The knowledge of the development and management of database systems is a must for data engineers. They should be fluent in programming languages such as SQL, Python, R, etc., and should have a basic understanding of machine learning and algorithms. Data engineers should also know about warehousing solutions and ETL (Extract, Transfer, Load) tools. Data science is a highly collaborative discipline, and data engineers collaborate with a variety of stakeholders, ranging from data analysts to Chief Technological Officers. Therefore, soft skills, like good communication skills and high cooperation skills, should be part of every data engineer's skill set.

3Is data engineering a good career path? How much does a data engineer make on average?

According to the Dice 2020 Tech Job Report, data engineering is the fastest-growing career option in technology in 2019, with a 50% year-on-year increase in the number of available opportunities. It is gaining significance in the technological world and has become a lucrative career option as the demand for information management grows. With an entry-level salary of ₹4,57,532, the wages of data engineers go up with the increasing years of experience. Data engineers with 1-4 years of experience earn an average salary of ₹7,20,395, whereas mid-career data engineers with 5-9 years of experience and experienced data engineers with 10-19 years of experience earn an average total income of ₹12,94,336 and ₹18,67,992, respectively.

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