Data Engineer Interview Questions

Big data is transforming how businesses operate, thereby increasing the demand for data engineers who can collect and organize massive amounts of information.

Being a data engineer requires a lot of work and is a demanding career. You need to be ready for data science challenges that might come up in an interview if you're a data engineer. 

Many problems have multiple steps to them, so planning them enables you to outline solutions as you progress through the interview process. 

Here, you'll learn about frequently asked data engineering interview questions and find answers that will help you ace the interview.

To make the learning process for the interview easier, we have divided the interview questions into three categories. They are

• For Freshers
• For Experienced
• FAQ's

Top 10 Data Engineer Questions

1. What is Data Modelling?
2. How to deploy a Big Data solution?
3. What are the daily responsibilities of a Data Engineer?
4. Explain Block Scanner and Block in HDFS.
5. What are Hadoop's various XML configuration files?
6. What are the Features of Hadoop?
7. Describe the main ways to use Reducer.
8. Describe the Hadoop distributed file system.
9. What are the Components of Hadoop?
10. What are the Big Data categories?

Top Data Engineer Interview Questions for Freshers

1. What is Data Engineering?

Data engineering is another name frequently used to refer to big data. The gathering of data and conducting of research are the primary focuses of this section. Simply put, the data produced by the various sources are unprocessed. The process of transforming this raw data into informative material is aided by data engineering.

2. What is Data Modelling?

Data modeling is a technique for simplifying complex software design so everyone can easily understand it. It is a conceptual illustration of the relationships between different data objects and the rules. It is a representation in the abstract of data objects that are linked together by rules.

If you want to become a Data Engineer, prepare yourself by joining the Data Science Online Training Course.

3. What are some of Data Engineers' most common issues?

Some of the most common issues that data engineers face are

• Data Management
• End User Understanding
• System Integration
• Regulatory Compliance
• Human Errors

4. What should one expect as a Data Engineer?

The responsibilities of a data engineer include collecting, managing, and transforming unstructured data into knowledge that both data scientists and business analysts can utilize. Accessibility of data, which grants companies the ability to use data for the purpose of performance evaluation and improvement, is their ultimate goal.

5. What information does a Data Engineer need to know?

Data engineers need to have expertise in various areas, such as databases, data infrastructure construction, containerization, and big data frameworks. The ideal candidate also has hands-on experience with numerous technologies such as Hadoop, Scala, Storm, HPCC, MapReduce, Rapidminer, Cloudera, SAS, SPSS, R, Python, Python, Kubernetes, Docker, and Pig.

6. What are the biggest issues with Big Data?

Businesses have to deal with many issues related to Big Data. Here are a few of the problems:

• Data quality, storage
• Requirement for data science experts
• Data validation
• Data aggregation

7. How to deploy a Big Data solution?

Below are the steps you should take if you want to implement a big data solution.

• MySQL, SAP, RDBMS, and Salesforce can all be used to integrate data.
• Put the information you've gathered into an HDFS or NoSQL database.
• Use processing frameworks like Spark, Pig, and MapReduce to roll out your big data solution.

8. What are the daily responsibilities of a Data Engineer?

Your ability to answer this question will be tested as a data engineer. Some of the most important duties of a data engineer are as you describe them below.

• Design, implementation, and upkeep of structural systems.
• Designing in accordance with operational needs.
• Processes for gathering information and building databases.
• Using statistical and machine-learning models
• Building data transformation and ETL processing pipelines
• Facilitating better data de-duplication and construction by streamlining the data-cleansing process.
• Improving the dependability, adaptability, accuracy, and quality of data is a top priority, so it's important to pinpoint where improvements can be made.

9. How can data analytics and Big Data help a company make revenue?

The ways in which data analytics and big data can boost business earnings are as follows:

• Make good use of data to promote the company's expansion.
• Boost the benefits to the customer base.
• Using data analytics to refine staffing projections.
• Lessening the company's production costs.

10. Do Data Engineers gather data?

Data engineers collect and clean data for use by data scientists and analysts. Data engineers often work in small groups to collect, ingest, and analyze data from start to finish.

Top Data Engineer Interview Questions for Experienced

11. Why are you interested in the Data Engineer position at our company?

• The person conducting the interview wants to know how much preparation you put into researching this position before applying for it. 
• When responding to this question, be sure to provide a concise explanation of how you would go about developing a plan that is compatible with the organization's structure and how you would go about putting that plan into action, ensuring that it is successful by first gaining an understanding of how the organization manages its data infrastructure. 
• In addition, be sure to explain how you would develop a plan that is compatible with the organization's structure and how you would develop a plan that is compatible with the organization's structure. 
• If you read job descriptions and research the company, it will be easier for you to respond to the question.

12. Have you experience working with big data in the cloud?

This question gives the interviewer insight into your level of preparedness to work in the cloud, which is where most businesses are moving to shortly. 

The benefits of cloud computing and your familiarity with the cloud environment should be highlighted in your application.

• Security and mobility.
• Its flexibility and scalability.
• Risk-free data access from anywhere.

Related Article: Big Data in AWS

13. Explain Block Scanner and Block in HDFS.

A data file is broken down into its most basic unit, which is called a block. Hadoop will automatically break down large files into more manageable chunks for you to work with. The Block Scanner is responsible for ensuring that the list of blocks that are presented on a DataNode is accurate.

14. What happens if Block Scanner finds a flawed data block?

When Block Scanner detects a bad data block, it will take the following steps:

• When the Block Scanner discovers a problematic data block, DataNode immediately notifies the NameNode.
• Whenever a block replica becomes corrupt, NameNode will begin the process of creating a new replica from scratch.
• A comparison is made between the replication factor and the number of reliable copies. In the event of a match, the corrupted data block will remain in place.

15. What are Hadoop's various XML configuration files?

Different types of Hadoop XML configuration files:

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

16. What are the Features of Hadoop?

Here are a few of Hadoop's most notable Features

• It's a free, open-source framework that anyone can use.
• Hadoop is compatible with many hardware services, making adding new hardware to a node easy.
• With Hadoop, processing data at scale can happen much more quickly.
• In this way, the data is protected from interference with the cluster's other tasks.
• Hadoop enables the creation of three identical copies of each block across multiple nodes.

Related Article: Hadoop Tutorial

17. Describe the main ways to use Reducer.

• Configuration (): Input data size and the location of the distributed cache are both determined at this stage.
• Cleanup (): During this process, temporary files are deleted.
• Reduce (): To accomplish the reduced task, the Reducer's core is contacted just once for each key.

18. List some of the most important fields or languages a Data Engineer uses.

Typically, data engineers specialize in one or more of the below-mentioned domains or programming languages:

• Machine learning
• Probability as well as linear algebra
• Hive QL and SQL databases
• Trend analysis and regression

19. What happens when the block scanner finds a bad data block?

When the block scanner discovers a bad data block, the following steps are carried out

• In the first place, DataNode will send a notification to NameNode whenever the Block Scanner finds a data block that has been corrupted.
• NameNode initiates the process of constructing a new replica by using a corrupted block replica as the starting point.
• A comparison is then made between the replication factor and the replication count of the good replicas. The corrupt data block will not be removed from the database if a match is found.

20. Describe the Hadoop Distributed File System.

Hadoop is compatible with many scalable distributed file systems, including HFTP S3, HDFS (Hadoop Distributed File System), and File System (FS). The Google File System is the foundation upon which the HDFS was constructed. This file system was developed to function smoothly across a large-scale distributed computing environment.

Related Article: What is HDFS?

Frequently Asked Data Engineer Interview Questions

21. Describe the primary responsibilities of a data engineer.

The work of a data engineer encompasses a wide variety of responsibilities. They are responsible for the system that serves as the data source. Data engineers are responsible for eliminating redundant data and simplifying complex data structures. Additionally, ELT and data transformation services are frequently provided well.

22. What are the Components of Hadoop?

Hadoop has the following components

• Hadoop Common: Various Hadoop-related software packages and resources.
• Hadoop HDFS: The Hadoop Distributed File System is the location where Hadoop stores its data (HDFS). HDFS is used to store data in a decentralized manner. A name node and a data node are the constituent parts of the HDFS file system. Although there will only ever be one name node, there could be many data nodes.
• Hadoop MapReduce: MapReduce functions as the processing unit for Hadoop. In the MapReduce technique, the processing is carried out on the agent nodes, and the primary node receives the result of the work once it is complete.
• Hadoop YARN: Yet Another Resource Negotiator is what YARN, which is part of Hadoop, stands for. The Hadoop resource management unit, a component of Hadoop version 2, is included in the Hadoop distribution. It is in charge of managing the cluster's resources to prevent any one machine from becoming overloaded.

23. Name the port numbers where Hadoop's NameNode, Job Tracker, and Task Tracker run by default.

Default Hadoop port numbers for the NameNode, task tracker, and job tracker are as follows:

• NameNode uses Port 50070.
• The task tracker uses port 50060.
• Job Tracker uses port 50030.

24. What exactly do you mean by "rack awareness"?

When writing or reading any file that is located closer to the nearest rack to the Write or Read request, the Namenode in the Hadoop cluster uses the Datanode to improve the flow of network traffic. To compile rack information, Namenode maintains a record of the rack id for each DataNode. Within Hadoop, this concept is referred to as Rack Awareness.

25. What does the Distributed Cache in Apache Hadoop do?

Hadoop contains a feature called Distributed Cache, which is a helpful utility that caches files that are used by applications. This speeds up work. Using the JobConf settings, an application can specify a file to be used for the cache.

These files are copied to all of the Hadoop framework nodes involved in a process that needs to be finished. This is done in advance of the actual task being carried out. Read-only files, zip files, and jar files can all be distributed successfully because of distributed caching.

26. Can a Data Engineer handle an ETL?

ETL is also considered a part of data engineering because data engineers are skilled at collaborating with various systems and technologies to get data ready for consumption. The data engineering process involves ingesting, transforming, delivering, and sharing data so it can be analyzed.

27. Are Data Engineers programmers?

As a data engineer, you will work with various computer languages, so you must have strong coding skills. Shell Scripting, Perl, and.NET R are a few popular programming languages in addition to Python. Java and Scala are essential because they allow you to work with MapReduce, a crucial Hadoop component.

28. Are APIs created by Data Engineers?

Data engineers use tools such as SQL and Java to gain access to data stored in source systems and transfer that data to target locations. The construction of distributed ETL pipelines is accomplished with Python.

29. What is the difference between a Data Scientist and a Data Engineer?

The difference between the data engineer and data scientist is described below

Data Scientist: Data science is a vast area of study. It focuses on data extraction from very large datasets (sometimes known as "big data"). Data scientists can work in many different sectors, such as business, government, and applied sciences. The same objective drives all data scientists: to examine data and draw conclusions about it that are pertinent to their line of work.

Data Engineer: A data engineer's duties include creating or integrating various system components while considering the information requirements, business objectives, and end-user needs. This calls for the development of very complex data pipelines. These data pipelines take raw, unstructured data from many sources, just like oil pipelines do. Then, they direct them into a single database (or other larger structure) for storage.

30. Do data engineers test the products they create?

Data engineers are responsible for ensuring that all data assets pass multiple data quality checks, which are required of all data assets. Numerous examples exist, including workflows for data pipelines, ETL scripts, and jobs. They collaborate with various stakeholders to review the requirements for complex data systems and develop test plans for those systems.

31. What are the Big Data categories?

The three categories of Big Data are 

• Structured Data
• Unstructured Data.
• Semi-structured Data.

Related Article: How to Become a Big Data Engineer

Conclusion 

Data engineering encompasses the wider fields of data collection, curation, and collection. No matter how big or small, any company can monitor its progress with these tools. Use the provided frequently asked data engineer interview questions to help you ace your interview. In addition, we provided all the answers related to the interview questions to land a position at your ideal company.