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With that being said, we would first like to clearly define the roles and responsibilities of a data engineer before we begin the interview prep.
Data Engineer as a career
A data engineer’s main job is to construct a robust data pipeline for an organization, which should be able to handle vast chunks of data. Also, a data engineer should tweak the architecture in such a way that it incorporates the ability to extract data from multiple sources. As a data engineer, you will find yourself working in conjunction with data scientists and cloud backend engineers and creating a mutually agreed solution by everyone working with Big Data in your organization.
On paper, your job might look well chalked out; however, in practice, that’s rarely the case. Many a time, the skill set you are supposed to have overlap with other roles that come under the umbrella of Big Data handling. You will find yourself working back and forth and sometimes having to do everything from the collection to the production of the model by yourself. This case is prevalent in those organizations which lack the needed workforce (like a startup); however, this issue all but vanishes once you start to work for a well-established organization.
So, we have tried to make a list of all the things you will be doing as a data engineer. Have a look:
- Finding out various sources for data and creating a way to collect all the data you found
- Performing the ETL (Extract, Transform, and Load) process
- Plugging the data that you formed into databases, be it SQL or NoSQL. Then, you would be tasked with rating all the databases formed and improving the ones with low scores
- Creating complex yet robust data pipelines
- Taking all the code which, you have written and put it into production
- Post-production, you would be tasked with creating robust metric systems to evaluate and rate the performance of models
Top 10 Data Engineer interview questions
Listed below, you will find the top 10 data engineer interview questions.
Q1. What do you mean by the term, data modeling?
Ans. In easy words, data modeling could be understood as the act of documenting complex and complicated designs of software in the form of a diagram that could be very easily interpreted. At its core, data modeling is just representing data objects conceptually.
Q2. What are the various design schemas which are used for data modeling?
Ans. In practice, there are only two design schemas that are used for data modeling. We have listed both of them below:
- Star Schema
- Snowflake Schema
Q3. What are all the components of any application which is based on Hadoop?
Ans. Many components come to mind when we think about any Hadoop-based application. We have listed them below:
- Common Hadoop: It happens to be the collection of all the famous and most used libraries in the production of Hadoop-based applications.
- HDFS: This is actually the central file system that is used for any Hadoop-based application.
- MapReduce: It is the algorithm that is used to tackle large-scale processing of Big Data.
- YARN: It is the Hadoop equivalent of resource management.
Q4. What do you mean by NameNode?
Ans. NameNode is at the heart of the HDFS storage system. It is used to store and track all the different files which are available across all the clusters.
Q5. What do you mean by streaming in the context of Hadoop?
Ans. It is the thing that is used to create maps, which in turn help with the reduction of jobs on any given cluster.
Q6. What happens to be the full form of HDFS?
Ans. HDFS actually refers to the Hadoop Distributed File System.
Q7. What are the various XML configuration files which you would be able to find in Hadoop?
Ans. There are quite a few XML configuration files that Hadoop offers. We have listed some of them below:
- Core-site
- Mapred site
- YARN site
- HDFS site
Q8. What do you think are the four Vs of Big Data?
Ans. The four Vs in the domain of Big Data are:
- Variety
- Velocity
- Volume
- Veracity
Q9. What do you think is the full form of COSHH?
Ans. The full form of COSHH is Classification and Optimization Based Schedule for Heterogeneous Hadoop Systems.
Q10. What do you think FIFO scheduling means in the context of data engineering?
Ans. In data engineering (mainly Hadoop), all the jobs which are supposed to be performed happen on the First in First Out basis or FIFO basis. In other words, the oldest pending job would be completed first. It can be better understood as a queue. The first person to be in the queue also happens to be the first one leaving.
We hope that you were able to find some really enticing questions for the next time you have your data engineer interview. If you find yourself lacking in any regard, find yourself a good data engineering certification that will help you forward your career in the long run. After all, it is clear that data engineering is part of the future.
You may also like: The Top Careers in Data Science
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