Data Engineer Interview Questions and Answers
New to the world of big data? Secretly wishing to break into a data engineering role. Already an experienced Data Engineer but looking for tremendous growth in this field? To answer all these questions, we have created this article with the most asked Data Engineer Interview Questions. According to one survey, the scope of data scientists grew by 10% by 2021 while data engineers take this percentage to 40% in 2020 which makes jobs for data engineers the fastest-growing job. When data was collected from over 500 tech companies, they concluded that for the data scientist role there was a 15% decrease in job growth in 2020 versus 2019. And this decrease is due to emerging growth in other data-related roles like data engineers and business analysts. The future of data engineers looks bright and prominent as companies will always use the collected data to enhance their business and that means data engineers will always be in demand.
Most Frequently Asked Data Engineer Interview Questions
Here in this article, we will be listing frequently asked Data Engineer Interview Questions and Answers with the belief that they will be helpful for you to gain higher marks. Also, to let you know that this article has been written under the guidance of industry professionals and covered all the current competencies.
| Data Engineering | Data Modelling |
|---|---|
| Converting the raw data into useful information is known as Data Engineering. | Simplification of complex application designs by breaking them up into simple workflow is known as Data Modelling. |
| Its main focus is on data collection and on research. | Its focus is to produce consistent and structured data. |
| The goal is to make data accessible so that companies can evaluate and optimize their performances. | The goal is to identify the types of data used, relationships among these data, and how they are organized. |
Hadoop and big data are the terms related to each other as Hadoop is the tool that is most commonly used for processing big data and is used in all the big companies such as Amazon, Facebook, Walmart, Google, etc. and one should be familiar with its components. It comprises mainly four components.
- Hadoop Common- It is the collection of Hadoop tools and libraries.
- Hadoop HDFS- Hadoop HDFS is Hadoop Distributed File System and is the storage unit of Hadoop which stores data in a unique distributed way. It comprises two parts a) Name node and b) Data Node. While there is always one Name Node, numerous Data nodes are possible in Hadoop.
- Hadoop MapReduce- It is the processing unit of Hadoop and is done on the slave node and the final output is sent to the master node.
- Hadoop YARN- YARN stands for Yet Another Resource Negotiator and is the resource management unit of Hadoop. It basically manages the cluster resources to avoid overloading a single machine and this component is included in Hadoop Version 2.
| STAR Schema | SNOWFLAKE Shema |
|---|---|
| In Data Warehousing star schema is one of the simplest schemas. | A Snowflake schema is a complex one as it contains more dimensions. |
| The structure looks like a star which consists of fact tables and associated dimension tables. | Data is structured in the snowflake form and split into more tables after normalization. |
| It has simple database designs | It has complex database designs and data handling storage. |
| Fast cube processing is done in a star schema. | Slower cube processing is done in a snowflake schema. |
| It has high chances of data redundancy. | It has low chances of data redundancy. |
| In this schema, the dimensions hierarchy is stored in the form of dimensions tables. | In this schema, hierarchy is stored in the form of an individual table. |
The interviewer wants to know if you can make decisions in stressful situations and want to understand what actions you will take.
“As the job is related to big data, which is very useful to manage, I can understand the responsibilities of data engineers. And it is very common to face different challenges in this job. If data is corrupted or gets lost, I will work with the IT department to make sure that a backup of this data is ready to get loaded and I will ensure that other team members have access to the data they need.”
The interviewer asked this data engineer question to take an idea of your understanding of the role of a data engineer and its job description.
- A data engineer can be involved in multiple areas such as architecting, building, and maintaining the big data infrastructure.
- They can also be involved in development and testing areas.
- They should know how to align the design with respect to business requisites.
- Should have knowledge of developing pipelines for various ETL operations.
- A data engineer should spot ways to improve the reliability, accuracy, quality, and flexibility of data.
- Should suggest some simple ways for data cleansing and improving the de-duplication of data.
Hadoop streaming is a feature provided by Hadoop that allows its developers or programmers to easily write the Map-Reduce program using programming languages such as C++, Ruby, Perl, Python, etc. The developer can use any programming language that can read from standard input (STDIN) and write using standard output (STDOUT). Users can easily create maps, perform reduction operations, and submit this into a cluster for usage.
- When the Block Scanner detects any corrupted file or corrupted data block the DataNode sends a message to the NameNode.
- After receiving a notification from DataNode, NameNode starts the process of, making a replica from corrupted block data.
- -Replication factor is compared to the count of right replicas and the corrupted data block won’t be deleted if a match is detected.
| Data Architect | Data Engineer |
|---|---|
| Data Architects mainly visualize and conceptualize the frameworks. | Maintenance and building of those frameworks are done by Data Engineers. |
| A data architect involves in the system development part. | A data engineer creates and designs the data applications. |
| They provided the organizational data blueprint. | They worked on the blueprint provided by data engineers. |
| They have deep knowledge of databases,operating system, data modeling, data architecture, etc. | They have deep expertise in algorithms, software engineering, and application development. |
| Their main focus is on leadership and high-level data strategy. | They handle the day-to-day task of cleaning preparing, and managing data for consumers and data scientists. |
| A data architect uses various ETL tools,spreadsheets, and various business intelligence tools. | They collect and process the raw data. |
No matter which is the organization and what is the job role, it ultimately comes to business growth and revenue generation.
- Big data analytics helps in setting realistic goals for an organization and supports decision making.
- By using data effectively and efficiently for business growth.
- By improving staffing and manpower forecasting methods.
- By decreasing the production cost of an organization.
- By increasing customer value and retention analysis.
- By creating a backup of important data in case of any job-related crisis or an emergency.
By opting for the below ways data security can be achieved in Hadoop.
- The first step is to secure the authentication channel which connects clients and the server and to provide a timestamp to the client.
- With the help of time-stamped the client requests a TGS for a service ticket.
- Finally, using this service ticket, a client can do self-authentication to the corresponding server.