Top 50 Data Engineer Interview Questions And Answers

Top 50 Data Engineer Interview Questions And Answers

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Data Engineering is one of the best and most sought fields, which is the fastest-growing job option globally. Companies always want professional data engineers for their team, so they interview every candidate thoroughly. They look for specific knowledge and skills; candidates have to be prepared accordingly to meet interviewer expectations.

Being prepared for a Data Engineer interview with a question type that the interviewee might ask is an excellent way to acing the interview. No matter how experienced the candidate is because the interviewer’s questions usually target multiple areas such as compatibility of the interviewee, leadership skills, project management skills, knowledge of technical tools, and so on. 

Here Wissenhive has collected the top 50 data engineer interview questions to help you in preparing for the data engineer field role.

1. What do you understand by the term Data Engineer?

Data Engineering refers to a term that is used while working on Big Data. Data Engineer focuses on performing data collections and in-depth research on the application by generating raw data from various sources. Through data engineering, raw entity data can be converted into useful and beneficial information.

 

2. What are the roles and responsibilities of a Data Engineer?

The roles and responsibility of Data Engineer covers a wide array of things, but some of the important are

• Processing pipelines and handling data inflow
• Managing data staging areas
• ETL data transformation exercises responsibility
• Elimination of redundancies and performing data cleaning
• Creating operations, building ad-hoc, and extraction of native data methods.

 

3. Difference between Data Scientist and Data Analytics?

Scope	Data Scientist	Data Analytics
Background	It deals with various data operations.	It is related to data cleansing, transforming, and generating inferences from data.
Scope	Involves several underlying data procedures	Involves limited small data and static inferences
Data Type	Manages structured and unstructured data	Manages structured data only
Skills	Processes knowledge of statistics, mathematics, and learning algorithms	Has problems solving skills and knowledge of basic statistics.
Tools	Proficient in Python, SAS, TensorFlow, R, Spark, and Hadoop.	Knows SQL, Excel, and R, and Tableau

 

4. What do you mean by Data Modeling?

Data modeling refers to the simplification method for documenting complex designs of software in the form of diagrams for easy understanding without any prerequisites. It offers numerous benefits, such as simple conceptual and visualized representation of data objects, associated between data objects and their rules. 

 

5. What are the various types of design schemas performed in Data Modelling?

There are two different types of design schemas performed in Data Modelling, and those are 

1. Star Schema 
2. Snowflake Schema

 

6. Differentiate between structured data and unstructured data?

Parameters	Structure Data	Unstructured Data
Storage Strategies	DBMS	Unmanageable structure of the file
Protocol Standards	SQL, ADO.net, and ODBC	CSV, SMSM, XML, and SMTP
Integration Tool	ELT	Batch processing or Manual data entry that includes codes
Scaling Level	Difficult	Easy
Example	Ordered dataset text file	Photos, videos, etc.

 

7. What do you understand about the Hadoop Application?

Hadoop refers to a set of open-source software framework utilities that facilities by using many computers’ networks to solve a massive amount of computation and data-related problems. It provides the software framework for Big data processing and distributing storage by using the MapReduce programming model.

 

8. What are the main components of the Hadoop Application?

There are several components required while working on the Hadoop application, but some of the popular components are

• Hadoop Common: Consist of a standard set of libraries and utilities 
• HDFS: Used for storing data and providing a distributed file system having high bandwidth.
• Hadoop MapReduce: Used for managing resources and scheduling task
• Hadoop YARN: Provides access to users for large-scale data processing.

 

9. What is Heartbeat in Hadoop?

In the Hadoop application, DataNode, and NameNode establish communication together. It refers to a signal which DataNode usually sends to NameNode to show its presence regularly.

 

10. What do you understand by the term Hadoop streaming?

Hadoop streaming refers to widely used Hadoop utilities for creating maps, performing various reduction operations, and submitting specific clusters.

 

11. What are some features available in Hadoop?

• It is an open-source framework. 
• Works on distributed computing basis
• Uses parallel computing for processing data faster
• Store data in separate clusters 
• It gives data redundancy to ensure no loss of data.

 

12. What are the three different Hadoop usage modes?

There are three different modes used in Hadoop, and those are

• Standalone mode
• Fully distributed mode
• Pseudo distributed mode

 

13. How is data security assured in the Hadoop application?

There are some steps that help in securing data in Hadoop Application.

• Begin with securing genuine and authentic channels, which helps in connecting clients to the platform. 
• Consumers are making the usage of stamps received to request a service ticket.
• Clients make the usage of service tickets for correcting the corresponding server authentically.

 

14. What is a NameNode?

• NameNode is one of the centerpieces or vital parts of HDFS. It helps in storing HDFS data and tracking multiple files from the clusters. However, the actual data does not store the information in NameNodes but is stored in DataNodes.

 

15. What are the main functions of secondary NameNode?

• Fsimage - It stores a copy of the FsImage file and EditLog
• NameNode Crash - If the original NamaNode crashes, then secondary Fslamage of Namenode can be used to create the NameNode again.
• Update - It helps in automatically updating the Fsimage and EditLog file to keep the Fsimage file updated on secondary NameNode.
• Checkpoint - Secondary NameNode uses checkpoint to check if data is secured or not in HDFS.

 

16. How does the DataNode communicate with the NameNode?

There are two different methods that help DataNode and NameNode in communicating via messages, and those are 

• Block reports
• Heartbeats

 

17. What are the 4V’s of Big Data?

• Variety
• Velocity
• Volume
• Veracity

 

18. Define HDFS Block and Block Scanner?

HDFS Block refers to a single data entity, which is considered the smallest factor. Blocks automatically divide files into a smaller section when Hadoop encounters a large file.

HDFS Block Scanner verifies whether Hadoop created loss-of-blocks is updated on the DataNode successfully or not.

 

19. How does Block Scanner handle corrupted files?

• DataNode reports NameNode about a particular file when the block scanner finds any corrupted file in the system.
• Then NameNode processes files by using original corrupted files to create replicas.
• If created file replicas matches and replications get blocked, then it saves the corrupted data block instead of removing it.

 

20. What do you understand by COSHH?

The full form of COSHH is Classification and Optimization-based Scheduling for Heterogeneous Hadoop systems. The COSHH provides detailed scheduling at both the application and the cluster levels to have a direct positive impact on the job completion times.

 

21. Differentiate between Snowflake schema and Star schema?

Parameters	Snowflake Schema	Star Schema
Data stored in	Individual tables	Dimension tables
Redundancy	Low data	High data
Processor	Slower cube processor	Fast cube processor
Data presentation	Complex data-handling storage	Simple database designs

 

22. What are some of the Hadoop XML configuration files?

There are four different types of Hadoop XML configuration files available or presented in Hadoop, and those are:

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

 

23. What is Combiner in Hadoop Application?

Combiner refers to an optional step between Reduce and Map. It takes all the necessary output from the Map function, building the key-value pairs, and submitting to the Hadoop reducer. Combiners’ main task is to summarize the final result into summary records from Map with an identical key.

 

24. What are the three main methods of Reducer?

The three main methods of Reducer are 

• Setup () - For configuring parameters
• Cleanup () - For clearing temporary files
• Reduce () - For reducing associated task

 

25. What do you mean by Star Join Schema?

Star Join Schema or Star Schema is one of the simplest type schemas for the Data Warehousing concept. This schema’s structure is like a star, consisting of multiple associated dimension tables and fact tables. It is widely used while working with large data sets.

 

26. What do you mean by Snowflake Schema?

The snowflake schema refers to a star schema’s primary extension with the presence of more dimensions. As the name suggests, the structure of this schema looks like a snowflake. It structures the data and, after normalization, split it into multiple tables.

 

27. What do you understand by FSCK? 

FSCK or file system check refers to one of the most important commands that HDFS uses. It is mostly used to check for file discrepancies, inconsistencies, and problems.

 

28. What are some of the important languages or fields used by data engineers?

Data engineers use few languages or fields, and those are

• Mathematics (Linear algebra and probability)
• Trend regression and analysis
• Machine Learning
• Summary statistics
• Python
• SQL and Hive QL databases
• SAS and R programming languages

 

29. What are the objects created by the CREATE statement in MySQL?

• Database
• Table
• Trigger
• Function
• Index
• Event
• User
• View
• Procedure

 

30. How to check the structure of the database in MySQL?

To check the structure of the database in MySQL, Data Engineers can use the DESCRIBE command.

 

31. What do you understand by Rack Awareness?

Rack Awareness refers to a process where NameNode takes access from DataNode to enhance the network traffic while writing or reading any document or file in the Hadoop cluster, which is a nearby rack to Write or Read request. NameNode manages the id of the rack of every DataNode to achieve information from the rack.

 

32. What are the default port numbers for Task Tracker, Port Tracker, and NameNode in Hadoop Application?

• The default port of Task Tracker: 50060
• The default port of Port Tracker: 50060
• The default port of NameNode Tracker: 50060

 

33. What are distributed file systems in the Hadoop application?

Hadoop application works with a distributed scalable file system such as 

• HFTP FS
• HDFS
• S3
• FS

The distributed file system of the Hadoop application is made on Google File System, which is designed to run with a large cluster on the computer system.

 

34. What is Big Data?

Big Data refers to a large amount of structured data and unstructured data, which is difficult to process by using traditional data storage methods. Data engineers prefer using the Hadoop application for managing large amounts of data.

 

35. How can Big Data Analytics improve a company's revenue?

Big Data Analytics helps many organizations in multiple ways, and those fundamental strategies are 

• Effective usage of data to relate with structured growth
• Forecasting manpower and improving staffing strategies
• Increasing customer value and analyzing retention
• Strategies of bringing down the cost of production majorly

 

36. What is the difference between NAS and DAS in Hadoop Application?

	NAS	DAS
Storage	10(9) to 101(2) in byte	10(9) in byte
Management cost	GB is moderate	GB is high
Transmit data	Ethernet or IP/TCP	IDE/ SCSI

 

37. What do you understand by FIFO scheduling?

FIFO (First In First Out) scheduling is one of the Job scheduling algorithms, which is also known as FCFS (first come, first served). In this scheduling, the reporter chooses the job from the queue of work, the oldest job first.

 

38. What are the complex data collection/types supported by Hive?

• Struct
• Union
• Map
• Array

 

39. What is the usage of context objects in the Hadoop Application?

A context object refers to a means of communication that is used in the Hadoop application with mapper class. It presents system configuration jobs and details in the constructor obtained easily by using context objects. Context objects are used in three methods to send information, and those methods are map(), setup(), and cleanup().

 

40. What is Data Locality in Hadoop Application?

In Big Data, the data size is huge, which makes it difficult to move huge data across the different networks. Here, Hadoop helps in moving computation closer to data, and in that way, the day remains stored to the location.

 

41. What is the main use of hive?

Hive in the Hadoop ecosystem is used to build the user interface to manage all the Hadoop stored data. HBase's data mapped tables work when needed. Hive queries are very similar to SQL queries that are executed and converted into MapReduce jobs, which can be done to keep to montage complexity when multiple executing jobs at once.

 

42. What are the three components available in the Hive model of data?

There are three different types of components available in Hive data mode, and those are 

• Tables
• Buckets
• Partitions

 

43. What do you understand by Hive’s Metastore?

Metastore in Hive is used to store locations for the Hive tables and Schema. Data such as mappings, definitions, and other metadata are stored in the Hive Metastore. Later, it started storing data in an RDMS. It uses metadata records to enable the MapR FS and Hadoop FS destinations to write parquet data or drifting Avro.

 

44. What are the functions available in Hive to create the table?

The important functions available in the Hive to creation table are

• Explode (Map)
• Explode (Array)
• Stack ()
• JSON-tuple ()

 

45. Explain the role of the .hiverc file?

The role and responsibility of the .hiverc file is initialization. When individuals want to create or write code for the hive, they can open up the command-line interface. .hiverc is the first to load when CLI opened while containing the parameters that the user initially set.

 

46. What are the uses of **kwargs and *args?

The **kwargs function is used for donating argument sets that are in line to be input and unordered to function. The *args function allows users to define an ordered function for the usage in the command line.

 

47. What do you understand about Skewed tables in Hive?

Hive’s skewed table refers to a special type of table that contains values column more often. The skewed table is a specific table in a hive, usually split into various separate files, and the remaining values go to the other files.

 

48. What do you understand about the Hive’s SerDe?

In Hive, SerDe’s full form is Serialization and Deserialization. It refers to an operation that involves passing records through the tables of the Hive. The Deserializer takes a record and changes it into an object of Java that the Hive understands. And then the serializer takes that object of Java and changes it into a processable format by HDFS. Later, HDFs take over the functions of storage.

 

49. What are the different types of SerDe implementations included in Hive?

There are several SerDe implementations available in Hive; you even can create your own custom. Some of the popular SerDe implementations are 

• RegexSerDe
• ByteStreamTypedSerDe
• DelimitedJSONSerDe
• OpenCSVSerde

 

50. Differentiate between Database and  Data Warehouse?

Scope	Database	Data Warehouse
Suited solution	OLTP Solutions	OLAP solutions
No. of users	It can handle thousands of users	It can handle a smaller number
Use	Recording data	Analysis data
Downtime	Always available	Scheduled downtime
Optimization	CRUD operations	Complex analysis
Data Type	Real-time detailed data	Summarized historical data

 

Here, this blog brings us to the end of the top 50 most asked questions by interviewers in a Data Engineering interview. Wissenhive covered almost every question that can help you in gaining and updating your knowledge to the next level and help you in clearing your interview.

Are you stuck with any questions? Feel free ''learners'' to mention it down in the comment section, and we will get back to you as soon as possible.

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