Top 25 Data Engineering Interview Questions & Answers in 2022

September 20, 2022

Are you new to the world of data engineering and looking to find an opportunity in a firm in this domain? Or, are you a veteran who is planning a switch? In that case, you need to be prepared for the interview.

Now, preparing for a data engineering interview can be exhausting. However, since this domain of data engineering is competitive, being prepared in advance will give you the upper hand. So, to help you in this endeavor, here is a list of some questions you will likely face in your interview for a data engineer.

Top 25 Data Engineering Interview Questions and Answers

Just have a look at the Data Engineering Questionnaire and see if you are aware of the following concepts.

  1. Why did you opt for a career in Data Engineering?

This is one of the staple data engineering interview questions. The aim here is to understand your intention and passion behind data engineering. It will also help the interviewer to assess how you will deal with your challenges every day.

How should you answer this question, then? Here is a sample:

I started as a data scientist when I came upon it by chance while studying in college. Then one of my friends also introduced me to online data engineering challenges on websites like Techgig, Leetcode and Kaggle. It got me hooked on data engineering, and I applied for an online challenge on Thoughtworks. Winners would be offered jobs/internships to clear the challenge. I participated and was among the top 3. I was offered an internship for 6 months, where I further polished my data engineering skills.  

  1. What are the core skills required for becoming a Data Engineer?

This is to check whether you understand what the field is all about. Usually, the new candidates, i.e. the fresher, face this question. Here is your template answer –

A data engineer needs to have the following core skills:

  • A good grasp of database design and architecture.
  • Proficient in both SQL and NoSQL DB systems.
  • Experienced in the storage of data stores and understanding distributed systems like Hadoop.
  • Experts in Data Warehousing and ETL tools.
  1. Define Data Modelling

Scientifically documenting complex data is called Data Modelling. It is done with the help of a diagram that gives the system's conceptual representation in the form of a picture. You can also mention any previous experience with data modelling in your career.

  1. What are the different types of design schemas in Data Modelling?

Mainly, there are two types of schemas in data modelling. Both these data modelling schemas are multidimensional models which are deployed for a data warehouse:

  • Star schema: A multidimensional model type which contains fact tables and 2D dimensional tables and uses fewer foreign-key joins. The shape of the model resembles a star.
  • Snowflake schema: A multidimensional model type which contains 3D tables, sub-dimension tables and fact tables which resemble the shape of a snowflake.
  1. What is the difference between a Data Warehouse and an Operational Database?

Databases which use SQL statements like Delete, Insert, and Update have a lot of speed and efficiency. As a result, analysing could be complicated. These are called Operational Databases. In comparison, a data warehouse has calculations, aggregations and select statements which are ideally suited for data analysis.

  1. What are *args and **kwargs used for?

This question is mainly for experienced data engineers who are applying for advanced roles.

  • *args is used to write an ordered function
  • **kwargs is used to write unordered arguments used in a function

Moreover, if you can write down the code for these functions by using them in a visual example, then it could show your expertise in this topic.

  1. What scripting languages do you know? Python, Java or Bash, or anything else?

This question is asked to check your understanding of scripting languages. As a data engineer, you should know scripting languages for efficiently performing analytical tasks and for dataflow automation. But keep in mind, mention that language to the interviewer in which you are most proficient.

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

The recruiter can judge whether you understand both the job roles, which are integral to the data warehouse team.

Data Engineers develop, test, and maintain the complete architecture for data generation, while data scientists examine and deduce complex data. They primarily focus on an organisation and its conversion of Big Data. Data engineers need to build the infrastructure for data scientists to work.

  1. How is the validation of data migration done from one database to another?

Data validation means ensuring no data is dropped. There are appropriate validation types in different scenarios. For example, Data validation could either be a simple comparison or it can also be done after the total migration.

  1. What is NameNode? What happens if NameNode crashes or is terminated?

NameNode is the central node of the Hadoop Distributed File System (HDFS). No actual data is stored in it. Instead, it stores metadata. NameNode is used to track the different files which are present in different clusters. Mostly, there is one NameNode, so after crashing or termination, the system may be unavailable.

  1. Which Python libraries would you utilise for proficient data processing?

It helps the recruiter to assess whether the data engineer is well versed with Python or not. Whatever your answer is, don't forget to include NumPy. NumPy is used for processing arrays efficiently. These arrays could have numbers. Also include Pandas, which have great statistical tools and help to prepare data for machine learning.

  1. What is the difference between Lists and Tuples?

Data structures have classes called Lists and Tuples. Lists can be edited, hence are mutable. But Tuples, on the other hand, cannot be changed and are hence immutable. Give examples if you want to.

  1. What are the names of the XML configuration files in Hadoop

The XML configuration files that are available in Hadoop are given below:

  • Mapred-site
  • Core-site
  • YARN-site
  • HDFS-site
  1. What is FSCK?

File System Check, which is also known as FSCK, is an important command of the HDFS. When you require checking of file errors and discrepancies, then it is useful.

  1. In HDFS, what are Block and Block Scanner?

When Hadoop sees a large file, it divides the file automatically into minor chunks called blocks which are the minimum data entity. Now, a block scanner ensures that the loss of blocked dues to Hadoop are installed on the DataNode without any interruptions.

  1. What is COSHH?

COSHH is an acronym for Classification and Optimisation-based Scheduling for Heterogeneous Hadoop Systems. It helps to schedule both the cluster and application levels to reduce the completion time of an assignment.

  1. How is Hive used in the Hadoop ecosystem?

Hive is used for providing a user interface, which helps to manage all of Hadoop's stored data. HBase is used to map and work on data as required. Hive queries help to synthesise MapReduce jobs. Moreover, it helps in the efficient management of the ecosystem when multiple jobs are being run at the same time.

  1. What is Rack Awareness?

Rack Awareness is used by NameNode, which further uses the DataNode for boosting incoming network traffic while running reading or writing operations simultaneously. It is performed on the file nearest to the rack from which the request is being made.

  1. What tools did you use in a recent project?

This question is asked to assess your decision-making skills and knowledge about different data engineering tools. This question is for experienced data engineers only and rarely put to freshers. But if you have ever handled a data engineering project, then you should be able to answer this question. Some examples are given below:

  • Big Query
  • Apache Spark
  • Apache Hive
  • Looker
  • Tableau
  • Airflow
  • Segment

You should be able to point out the tool's strengths and weaknesses to show you know how things work.

  1. Name the two messages that NameNode obtains from DataNode

NameNodes gets data from DataNodes in the form of signals or messages. The two signals are:

  • Block report signals on DataNode. These are related to lists of data blocks and their functioning.
  • Heartbeat signals to show that the DataNode is functioning and alive and functional. These signals send periodic reports to find out and assess whether to use NameNode or not.
  1. How can you deploy a big data solution?

There are important steps for deploying a big data solution –

  • Extracting data from data sources like Salesforce, RDBMS, MySQL or SAP.
  • Storing the extracted data in a NoSQL or HDFS database.
  • Processing frameworks like Pig, Spark and MapReduce
  1. How are duplicate data points dealt with in an SQL query?

Interviewers assess your SQL knowledge with this question. Suggest using SQL keywords like DISTINCT and UNIQUE for reducing duplicate data points. GROUP BY can also be used for dealing with duplicate data points.

  1. How can data analytics help the business grow and boost revenue?

Data analytics help the business to grow and generate revenue. Big Data analysis helps boost revenue, improve the customer experience, and increase profits. Data analytics helps to set realistic goals, hence helping in decision-making. Big Data analytics helps to grow 5%-20% of the revenue. Walmart, Facebook and LinkedIn are companies using big data analytics to grow their revenue.

  1. Do you possess any experience in building data systems using the Hadoop framework?

If you have used Hadoop before, answer by explaining in detail the project using your skills and how well you were able to use the framework. Explain all the important characteristics of Hadoop, and don't forget to mention its scalability and ability and speed of processing data while preserving the data quality.

Some features of Hadoop are as follows:

  • Since it is based on Java, it is easy to learn and use.
  • All the data storage is done within Hadoop. You can access the data in case the hardware fails at any instance
  • In Hadoop, data storage is done in cluster form, and it is hence, independent of other operations.
  1. What challenges came up during your recent project, and how did you overcome these challenges?

All employers want to understand your difficulty handling prowess and how you face challenges. You can frame your reply by employing the STAR method:

  • Situation: Narrate in brief how the problem happened.
  • Task: Tell them how you overcame the problem. This speaks a lot about your inborn leadership abilities.
  • Action: What steps did you take to fix the problem?
  • Result: What were the consequences of the actions you undertook? What did you and the other stakeholders learn about the situation?

Parting Thoughts

These are some of the questions that will certainly help you prepare for your data engineering interviews better. Now, a point to remember here is that there will be other technical questions barring the ones mentioned above, so prepare accordingly. Also, there will be other questions regarding your personal life and your soft skills, so make a point to revise them as well.

Nevertheless, if you find this content helpful, visit the 'Blog' section on the website of E2E Networks for more content on current development in the field of IT and communications.

Reference Links

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Comparison between Cloud-Based and On Premises GPUs

Cloud GPUs vs On Premises GPUs

Cloud GPUs are typically more powerful than on-premises GPU instances. The cost of renting a cloud GPU is generally lower than the cost of purchasing an on-premise GPU. 

Cloud platforms offer fast access to high performance compute and deep learning algorithms, which makes it simpler to start using machine learning models and get early insights into your data. 

Cloud GPUs are better for machine learning because they have lower latency, which is important because the time it takes a neural network to learn from data affects its accuracy. Furthermore, cloud GPUs allow users to take advantage of large-scale training datasets without having to build and maintain their own infrastructure.

On Premises GPUs are better for machine learning if you need high performance or require access to cutting-edge technologies not available in the public cloud. For example, on-premises hardware can be used for deep learning applications that require high memory bandwidth and low latency.

Cloud GPUs: Cloud GPUs are remote data centers where you can rent unused GPU resources. This allows you to run your models on a massive scale, without having to install and manage a local machine learning cluster.

Lower TCO: Cloud GPUs require no upfront investment, making them ideal for companies that are looking to reduce their overall capital expenses. Furthermore, the cost of maintenance and upgrades is also low since it takes place in the cloud rather than on-premises.

Scalability & Flexibility: With cloud-based GPU resources, businesses can scale up or down as needed without any penalty. This ensures that they have the resources they need when demand spikes but also saves them money when there is little or no demand for those resources at all times.

Enhanced Capacity Planning Capabilities: Cloud GPU platforms allow businesses to better plan for future demands by providing estimates of how much processing power will be required in the next 12 months and beyond based on past data points such as workloads run and successes achieved with similar models/algorithms etc... 

Security & Compliance : Since cloud GPUs reside in a remote datacenter separate from your business' core systems, you are ensured peace of mind when it comes to security and compliance matters (eigenvector scanning / firewalls / SELinux etc...) 

Reduced Total Cost Of Ownership (TCO) over time due to pay-as-you-go pricing model which allows you only spend what you actually use vs traditional software licensing models where significant upfront investments are made.

Cloud GPUs: Cloud GPUs offer significant performance benefits over on-premises GPUs. They are accessible from anywhere, and you don't need to own or manage the hardware. This makes them a great choice for data scientists who work with multiple data sets across different platforms.

Numerous Platforms Available for Use: The wide variety of available platforms (Windows, Linux) means that you can run your models using the most popular machine learning libraries and frameworks across different platforms without having to worry about compatibility issues between them.

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October 4, 2022

Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

As the rupee is inching closer to 82 per dollar, the strong dollar has directly impacted the costs of cloud services for Indian businesses. The high cost of storage and computing power, along with bandwidth charges from overseas vendors, has led to a huge increase in the effective rate of these services. This is especially true for startups and SMEs that rely on cloud computing to store and process user data. With the strong dollar continuing to impact the cost of cloud services, it is essential for Indian companies to evaluate their options and adopt local alternatives wherever possible. This blog post will discuss how the strong dollar impacts cloud costs, as well as potential Indian alternatives you can explore in response to this global economic trend. 

What is a Strong Dollar?

A strong US dollar($) is a term used to describe a situation where a US’s currency has appreciated in value compared to other major currencies. This can be due to a variety of factors, including interest rate changes, a country’s current account deficit, and investor sentiment. When a currency appreciates, it means that it is worth more. A strong dollar makes imports more expensive, while making exports cheaper. Strong dollars have been a growing trend in the past couple of years. As the US Federal Reserve continues to hike interest rates, the dollar strengthens further. The rising value of the dollar means that the cost of cloud services, especially from hyperscalers based in the US, will rise as well. 

Increase in Cloud Costs Due to Strong Dollar

Cloud services are essential for modern businesses, as they provide easy access to software, storage, and computing resources. Cloud services are delivered over the internet and are typically charged on a per-use basis. This makes them incredibly convenient for businesses, as they can pay for only the resources they actually use. Cloud computing allows businesses to scale their resources up or down, depending on their current business needs. This makes it suitable for startups, where demand is uncertain, or large enterprises with global operations. Cloud computing is also inherently scalable and allows businesses to quickly react to changing business needs. Cloud computing is a very competitive industry and providers offer attractive prices to attract customers. However, these prices have been impacted by the strong dollar. The dollar has strengthened by 15-20% against the Indian rupee in the last few years. As a result, the costs of services such as storage and bandwidth have increased for Indian companies. Vendors charge their Indian customers in Indian rupees, taking into account the exchange rate. This has resulted in a significant rise in the costs of these services for Indian companies.

Why are Cloud Services Becoming More Expensive?

Cloud services are priced in US dollars. When the dollar is strong, the effective price of services will be higher in Indian rupees, as the cost is not re-adjusted. There are a couple of reasons for this price discrepancy. First, Indian customers will have to pay the same prices as American customers, despite a weaker Indian rupee. Second, vendors have to ensure that they make a profit.

Possible Indian Alternatives to Cloud Services

If you're looking for a cost-effective substitute for services provided by the U.S.-based suppliers, consider E2E Cloud, an Indian cloud service provider. When it comes to cloud services, E2E Cloud provides everything that startups and SMEs could possibly need.

The table below lists some of these services and compares their cost against their US equivalents. 

According to the data in the table above, Indian E2E Cloud Services are much cheaper than their American equivalents. The difference in price between some of these options is substantial. When compared to the prices charged by suppliers in the United States, E2E Cloud's bandwidth costs are surprisingly low. Although not all E2E Cloud services will be noticeably less expensive. Using Indian services, however, has an additional, crucial perk: data sovereignty.


The price of cloud services will rise as the US Dollar appreciates. Indian businesses will need to find ways to counteract the strong dollar's impact on their bottom lines. To do this, one must use E2E Cloud. The availability of E2E Cloud services in INR currency is a bonus on top of the already substantial cost savings. An effective protection against the negative effects of a strong dollar.

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September 28, 2022

Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.


In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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