Top 70 Data Engineer Interview Questions and Answers

The data engineer’s main task is to find trends in the data sets and develop algorithms to make raw data more useful to the enterprise. Data engineers are responsible for building the algorithms to help give easy access to raw data, but in order to do this, they have to understand the company’s or client’s objectives.

If you have a Data Engineer Interview scheduled nearby, then you have to definitely prepare yourself for the interview. Preparing for an Interview is not a simple task. So before you attend the interview, make sure you go through the Data Engineer Interview Questions and Answers so that you can easily crack the interview. 

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TOP Data Engineer Interview Questions and Answers

1. Explain Data Engineering in simple terms?

Data engineering makes use of the tools such as SQL and Python to make the data ready for the data scientists. Data engineering works mainly with data scientists to understand their specific needs for the job. They will build data pipelines that source and transform data into the desired structures that are needed for analysis.

2. In what ways Python Help Data Engineers?

Data Engineers use Python to create the data pipelines, write the ETL scripts, and set up statistical models and perform the analysis like R, which is an important language for data science and data engineering. It is important for ETL, machine learning applications, and data analysis.

3. Differentiate between the Data Warehouse and Operational Database?

Operational DatabaseData Warehouse
They are designed to support high-volume transaction processing.They are typically designed to support high-volume analytical processing like OLAP.
They are concerned with current data.They are concerned with historical data.
Data here are mainly updated regularly according to need.Non-volatile, new data will be added regularly. Once added, it will be rarely changed.
They are designed for real-time business dealing and processes.They are designed for the analysis of business measures by subject area, attributes, and categories.
Less Number of data is accessed.A large number of data is accessed.

4.  Define Data Modelling?

Data modeling can be defined as a  technique used to define and analyze the data requirements that are needed to support the business processes within the scope of the corresponding information systems in the organizations. Data modeling defines not only the data elements but their structures and the relationships between them also.

5. Differentiate between Relational vs. Non-Relational Databases?

Relational DatabaseNon-Relational Database
They are also called relational database management systems (RDBMS) or SQL databases. They are also called NoSQL databases.
The popular Relational databases are Microsoft SQL Server, Oracle Database, IBM DB2, and MySQL.The most popular Non-Relational databases are MongoDB, DocumentDB, Cassandra, HBase, Redis, and Coachbase.
RDBMSs are usually used in large enterprise scenarios, which are mostly used to store data for web applications.They store large volumes of data without any structure.

6. Define Do *args and **kwargs?

*args and **kwargs are special keywords that allow the function to take the variable-length arguments. **kwargs are used to pass the variable number of keyword arguments dictionary to the function on which the operation of a dictionary is performed. *args and **kwargs usually make the function flexible.

7. Mention the various types of design schemas in Data Modelling?

There are two types of schemas in data modeling: 

  1.  Star schema 
  2.  Snowflake schema.

8. What are the technical skills required to be a data engineer?

  1. Database systems (SQL and NoSQL)
  2. Data warehousing solutions
  3. ETL tools
  4. Machine learning
  5. Data APIs.
  6. Python, Java, and Scala programming languages
  7. Understanding the basics of distributed systems
  8. Knowledge of algorithms and data structures

9. Differentiate between structured and unstructured data?

Structured dataUnstructured data
It is a clearly defined and searchable types of dataHere, the data is usually stored in its native format.
Structured data is quantitative.Unstructured data is qualitative.
Structured data is stored in data warehouses.Unstructured data is stored in data lakes.
It is easy to search and analyze.It requires more work to process and understand.  

10. Name the essential frameworks and applications for data engineers?

  1. Spark
  2. Flink
  3. Kafka
  4. Elastic search
  5. PostgreSQL/Redshift
  6. Airflow

Data Engineer Interview Questions and Answers

11. Explain the components of a Hadoop application?

  1. Hadoop Common: It can be defined as a set of utilities and libraries that are utilized by Hadoop.
  2. HDFS: The Hadoop application refers to the file system where the Hadoop data gets stored. It is a distributed file system that is having a high bandwidth.
  3. Hadoop MapReduce: It is based on the algorithm for the provision of large-scale data processing.
  4. Hadoop YARN: It is mainly used for resource management inside the Hadoop cluster. It is also used for task scheduling for users.

12. Differentiate between a Data Engineer and Data Scientist?

Data EngineerData Scientist
They mainly focus on building infrastructure and architecture for data generation.They focus on advanced mathematics and statistical analysis of the generated data. 
They support data scientists and analysts by providing infrastructure and tools that are used to provide end-to-end solutions to business problems.They are engaged in interaction with the data infrastructure that is built and maintained by data engineers.

13. Define NameNode?

Namenode can be defined as the master node that will runs on a separate node in the cluster. It manages the filesystem namespace that is the filesystem tree of the files and directories. It stores information such as owners of files, file permissions, etc., for the files.

14. What are the daily responsibilities of a data engineer?

Data engineer responsibilities are:

  1. They develop, construct, test, and maintain architectures.
  2. Data acquisition
  3. Develop data set processes
  4. Align architecture with business requirements
  5. They conduct research for industry and business questions
  6. Prepare the data for predictive and prescriptive modeling
  7. They use data to discover tasks that can be automated
  8. They make use of large data sets to address business issues.
  9. They find hidden patterns using data.

15. What is Hadoop streaming?

Hadoop streaming is a utility that usually comes with the Hadoop distribution. This utility allows us to create and run Map or Reduce the jobs with any executable or script as the mapper or the reducer.

16. Can you explain the design schemas in Data Modelling?

Schema can be defined as the logical description of the entire database. 

Some of the schemas in Data Modelling are:

Star Schema: Each dimension in the star schema is defined with only one dimension table. This dimension table consists of a set of attributes.

Snowflake Schema: The dimension tables in the Snowflake schema are normalized. This normalization divides the data into additional tables. Unlike the Star schema, the dimensions table in the snowflake schema is normalized.

Fact Constellation Schema: A fact constellation usually has multiple fact tables. It is also called galaxy schema.

17. What is the full form of HDFS?

 The Hadoop Distributed File System is a distributed file system that is designed to run on commodity hardware.

18. Explain the concepts of Block and Block Scanner in HDFS?

Block: It is defined as the minimum amount of data that is read or written.

The default size of the block in HDFS is 64MB. 

Block Scanner: It tracks the list of all the blocks present on the  DataNode, and it verifies them to find out any kind of checksum errors.

19. Name the two messages that the NameNode gets from DataNode?

NameNodes gets information about the data from DataNodes, generally in the form of messages or signals. They are:

  1. Block report signals: These are the list of data blocks that are stored on DataNode and its functioning.
  2. Heartbeat signals:  It is a periodic report that establishes whether to use NameNode or not. If this signal is not sent, then it means that the DataNode has stopped working.

20. Define the steps that occur when Block Scanner detects a corrupted data block?

The below steps will occur when a corrupted data block is detected by a block scanner:

  1. The DataNode will report the corrupted block to the NameNode.
  2. NameNode will then start the process of creating the new replica using a correct replica of a corrupted block that is present in other DataNodes.
  3. The corrupted data block is not deleted until the replication count of the correct replicas is matched with the replication factor.
  4. This entire process allows the HDFS to maintain the integrity of data when the client performs the read operation.

Data Engineer Interview Questions and Answers

21. Explain the Reducer phases and their core methods?

The Hadoop Reducer processes the data output of the mapper, and it produces the final output stored in HDFS. 

The Reducer mainly has 3 phases:

  1. Shuffle: Here, the output from mappers is shuffled, and it acts as the input for the Reducer.
  2. Sorting is done while shuffling, and at the same time, the output from different mappers is sorted. 
  3. Reduce: Here, the Reduces aggregates the key-value pair and gives the output, which is then stored on HDFS and is not further sorted.

There core methods in Reducer:

  1. Setup: This configures various parameters, such as input data size.
  2. Reduce: It is defined as the main operation of the Reducer. Here, a task is defined for the associated key.
  3. Cleanup: This method cleans up the temporary files at the end of the task.

22. Mention the various XML configuration files in Hadoop?

The XML configuration files in Hadoop:

  1. Mapred-site
  2. Core-site
  3. HDFS-site
  4. Yarn-site

23.  Explain how to deploy a big data solution?

The three significant steps used to deploy big data solution are:

  1. Data Integration/Ingestion: Here, the data extraction using data sources such as RDBMS, Salesforce, SAP, MySQL is done.
  2. Data storage: Here, the extracted data is stored in the HDFS or NoSQL database.
  3. Data processing: This is the last step that should be deploying the solution using the processing frameworks such as MapReduce, Pig, and Spark.

24. Mention the four V’s of big data?

Four V’s are:

  1. Velocity
  2. Variety
  3. Volume
  4. Veracity

25. List the pros and cons of working in CLoud computing?


  1. No administrative or management hassles
  2. Easy accessibility
  3. Pay per use
  4. Reliability
  5. Huge cloud storage
  6. Automatic software updates


  1. Limited control of infrastructure
  2. Restricted or limited flexibility
  3. Ongoing costs
  4. Security
  5. Technical issues

26. Explain some of the features of Hadoop?

A few of the Important features of Hadoop are:

  1. Hadoop is an open-source Java-based programming framework. The open-source indicates that it is freely available, and one can change its source code as per your needs.
  2. Fault Tolerance: Hadoop control faults by the technique of replica creation. When the client stores a file in HDFS, the Hadoop framework divides the file into blocks. 
  3. Distributed process: It stores a large amount of data in a distributed manner in the HDFS. It processes the data in parallel on the cluster of nodes.
  4. Scalability: As already stated, Hadoop is an open-source platform. Which makes it an extremely scalable platform
  5. Reliability: Data here is reliably stored on the cluster of machines in spite of the machine failure due to the replication of data. So, in case if any of the nodes fails, then also you can store data reliably.
  6. High Availability: Because of its multiple copies of data, the data here is highly available and accessible in spite of the hardware failure.
  7. Economic: It is not very expensive because it runs on a cluster of commodity hardware.

27. Name the Python libraries you would utilize for proficient data processing?

  1. NumPy
  2. SciPy
  3. Pandas
  4. Keras
  5. SciKit-Learn
  6. PyTorch
  7. TensorFlow

28. What is the full form of COSHH?

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

29. Differentiate between list and tuples?

They are mutable.They are immutable.
The list is preferred for performing operations, like insertion and deletion.The Tuple data type is appropriate for accessing elements.
They have several built-in methods.They do not have many built-in methods.
It consumes more memory.They consume less memory compared to lists.

30. Define Star Schema?

Star schema can be defined as the fundamental schema among the data mart schema, and it is the simplest one. This schema is mainly used to develop or build the dimensional data marts and data warehouses; It includes one or multiple fact tables indexing any number of dimensional tables. 

Data Engineer Interview Questions and Answers

31. How to deal with duplicate data points in an SQL query?

  1. We use the SQL RANK function to remove any duplicate rows. The SQL RANK function gives a unique row ID for each row disregarding the duplicate row.
  2. We use the Sort Operator in an SSIS package to remove duplicating rows.
  3. SQL deletes the duplicate Rows using the Common Table Expressions (CTE)
  4. SQL deletes the duplicate Rows by using Group By and having clause

32. Define Snowflake Schema?

Snowflake Schema in a data warehouse can be defined as the logical arrangement of tables in the multi-dimensional database in such a way that the ER diagram looks like a  snowflake shape. It is the extension of the Star Schema and adds additional dimensions. The dimension tables are normalized, which then splits the data into additional tables.

33. How data analytics help businesses grow and boost revenue?

  1. It helps you set realistic goals.
  2. It supports decision-making. 
  3. It helps you find your ideal demographic.
  4. You can segment your audience.
  5. It helps you create mass personalization.
  6. It helps to increase your revenue and lower your costs.
  7. You can boost your memberships.
  8. It helps you monitor social media. 

34. Define FSCK?

The system utility file system consistency check(fsck ) is a tool used for checking the consistency of the file system in Unix and the Unix-like operating systems, like Linux, macOS, and FreeBSD.

35. Differentiate between OLTP and OLAP?

OLTP is transactional processing.OLAP can be defined as an online system that reports to the multidimensional analytical queries such as  financial reporting, forecasting, etc
It is a system that can manage transaction-oriented applications on the internet like ATM.The OLAP solution enhances the data warehouse with aggregate data and business calculations.
It is an online database modifying system.It is an online database query answering system.
OLTP has short transactions.OLAP has long transactions.
Tables in the OLTP database are normalized (3NF).Tables in the OLAP database are not normalized.

36. Distinguish between Star Schema and Snowflake Schema?

Star SchemaSnowflake Schema
Here, only a single join creates the relationship between the fact table and the dimension tables.It requires many joins to fetch the data.
High level of data redundancyVery low-level data redundancy
Simple DB design.Very complex DB design
A single Dimension table contains aggregated data.Here, the data is split into different dimension tables.

37. What is the abbreviation of YARN?

The full form of YARN: Yet Another Resource Negotiator

38. What is the main concept behind the Framework of Apache Hadoop?

It is mainly based on the MapReduce algorithm. Here, in this algorithm, to process a large data set, we make use of the Map and Reduce operations. It maps, filters, and sorts the data while Reduce summarizes the data. Scalability and fault tolerance are the important points in this concept. We achieve these features in the Apache Hadoop by implementing MapReduce and Multi-threading efficiently.

39. Name the different usage modes of Hadoop?

The three different modes used by Hadoop are:

  1. Standalone mode
  2. Pseudo distributed mode
  3. Fully distributed mode

40. How can we achieve security in Hadoop?

  1. In the first step, we have to secure the authentication channel of the client to the server. You have to Provide time-stamped to the client.
  2. Next, the client uses the received time-stamped to request the TGS for the service ticket.
  3. Lastly, the client makes use of a service ticket for self-authentication to the specific server.

Data Engineer Interview Questions and Answers

41.  What are the steps to be followed while deploying a Big Data solution?

The steps to be followed while deploying a Big Data solution:

  1. Data Ingestion: It is the technique of collecting or streaming information from different sources such as log files, SQL databases, and social media files. It faces three important challenges: ingestion Schema changes, Large table ingestion in the source, and Change data capture.
  2. Data Storage-: After the data ingestion, the extracted data has to be stored somewhere. It should be stored either in HDFS or the NoSQL databases. HDFS works best for sequential access through the HBase for the random read or writes access.
  3. Data Processing: This is the last step for deploying on a Big Data solution. After data storage, the data is processed via one of the main frameworks like Pig or MapReduce.

42. Name the default port numbers for Port Tracker, NameNode, and Task Tracker in Hadoop?

  1. Task Tracker has the default port: 50060
  2. NameNode has the default port: 50070
  3. Job Tracker has the default port: 50030

43. Differentiate between NAS and DAS in Hadoop?

It Transmits data using Ethernet or TCP/IP.It Transmits data using IDE/ SCSI.
Its Management cost per GB is moderate.Its Management cost per GB is high.

44. Define the data stored in the NameNode?

The NameNode mainly consists of all of the metadata information required for HDFS, like the namespace details and individual block information.

45. What happens if the NameNode crashes in the HDFS cluster?

The HDFS cluster usually has only one NameNode, and it is used to maintain DataNode’s metadata. Having only one NameNode gives the HDFS clusters a single point of failure.

If the NameNode crashes, systems will become unavailable. To prevent this, you should specify a secondary NameNode that can take the periodic checkpoints in the HDFS file systems, but this is not a backup of the NameNode. But we use it to recreate the NameNode and restart it.

46. Define Rack Awareness?

Rack Awareness allows Hadoop to maximize the network bandwidth by favoring transfers of blocks within the racks over the transfer between the racks. With rack awareness, the YARN will optimize MapReduce job performance. It will assign tasks to the nodes that are close to the data in terms of network topology.

47. Name the important languages used by data engineers?

A few fields used by data engineer are:

  1. Machine learning
  2. Trend analysis and regression
  3. Probability as well as linear algebra
  4. Hive QL and SQL databases

48. What is a Heartbeat message?

The Hadoop Name node and the data node communicate using Heartbeat. Hence Heartbeat is a signal sent by the data node to namenode after a regular interval time to indicate its presence( to indicate that it is alive).

49. Define Big Data?

Big data is a term that is used to describe the large volume of data (both structured and unstructured)  that overruns a business on a day-to-day basis. It’s what the organizations do with the data that matters. Big data is analyzed for insights that lead to strategic business moves and better decisions.

50. Define Context Object in Hadoop?

The Context object allows the Mapper or Reducer to communicate with the rest of the Hadoop system. It includes configuration data for the job and interfaces that allow it to emit the output. Applications use the Context: to report the progress.

Data Engineer Interview Questions and Answers

51. Define FIFO scheduling?

Data engineer interview questions - FIFO scheduling

The original Hadoop Job Scheduling Algorithm that was integrated within the JobTracker is the FIFO. As a process, the  JobTracker pulled jobs from the work queue, which says the oldest job first. This is known as Hadoop FIFO scheduling.

52. What do we use Hive in the Hadoop ecosystem?

Hive is a bit of the Hadoop ecosystem and provides the SQL-like interface to the Hadoop. It is the data warehouse system for Hadoop that can facilitate ad-hoc queries, easy data summarization, and the analysis of huge datasets that are stored in Hadoop compatible file systems.

53. How is the distance between two nodes in Hadoop defined?

The distance is defined as equal to the sum of the distance to the closest nodes. We use the method getDistance() to calculate the distance between two nodes.

54. What do we use Metastore in Hive?

Metastore can be defined as the central repository of the Apache Hive metadata. It is used to store metadata for the Hive tables and partitions in a relational database. The clients can access this information by using the metastore service API.

55. Define commodity hardware in Hadoop?

It is computer hardware that is affordable and easy to obtain. Basically, it is a low-performance system and is IBM PC-compatible, and it is capable of running Linux, Microsoft Windows, or MS-DOS without any special devices or equipment. 

Data Engineer Interview Questions and Answers

56. Name the components available in the Hive data model?

The components in Hive:

  1. Buckets
  2. Tables
  3. Partitions

57. What is a replication factor in HDFS?

The Replication Factor is basically the number of times the Hadoop framework replicates each Data Block. Block is replicated in order to provide Fault Tolerance. The default replication factor will be three, which can then be configured as per the requirement; it can be changed to 2  or can be increased.

58. Is it possible to create more than a single table for an individual data file?

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

59. Can you explain the daily work of a Data Engineer?

  1. Handling data within the organization.
  2. Maintaining source systems of data and staging areas.
  3. Doing ETL  and data transformation.
  4. Simplifying the data cleansing and improving the data de-duplication and building.
  5. They have to do ad-hoc data query building and extraction.

60. List the collections that are present in Hive?

Hive has the below-mentioned collections or data types:

  1. Array
  2. Map
  3. Struct
  4. Union

61. What is a Combiner in Hadoop?

A Combiner, also called a semi-reducer, is an optional class that is operated by accepting the inputs from the Map class, and then it passes the output key-value pairs to the Reducer class. The function of a Combiner is to summarize the map output records with a similar key.

62. What are Skewed tables in Hive?

When there is a table with the skew data in the joining column, we use the skew join feature. It is a table that has values present in large numbers in the table when compared to other data.

63. Define Safe mode in HDFS?

Safemode for the NameNode is a read-only mode for the HDFS cluster, where it does not allow any other modifications to the file system or blocks.

64. Name the table creation functions present in Hive?

Below mentioned are some of the table creation functions in Hive:

  1. Explode(array)
  2. Explode(map)
  3. JSON_tuple()
  4. Stack()

Apart from technical questions, the interviewer will ask you some scenario-based questions which you have to answer based on your experience and the grip you have in Data Engineering. I have listed a few scenario-based and general questions that you might face in your Interview, make sure you get prepared with the below-mentioned questions also.

65. Have you trained someone in your field? What challenges have you faced? 

66. Have you worked with Hadoop Framework?

67. Which ETL tools are you familiar with?

68. Tell us a scenario where you were supposed to bring data together from different sources but faced some unexpected issues, and how did you resolve it?

69. According to you, what is the toughest thing about being a data engineer?

70. Why did you study data engineering?

Good luck with your Data Engineer Interview, and we hope our Data Engineer Interview Questions and Answers were of some help to you. You can also check our Data Analyst Interview Questions and Answers, which might be of some help to you.

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