Data engineering is one of the highest in-demand job profiles. If you are someone looking out to start a career in data engineering or want to switch careers to become a data engineer then this article can help you in your next data engineer interview. We have listed below some of the most commonly asked data engineer interview questions.
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Data Engineer Interview Questions
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Q1. How do you decide your approach to develop a new analytical product as a data engineer?
Ans. Data engineers are responsible to control the final product outcome and build algorithms or metrics with the correct data. Your interviewer is interested to know the role you have played in new product development and understand how much you know about a product development cycle.
In reply to this data engineering interview question, you should explain every step of your role in the product development cycle. Present the outline of the product development cycle to comprehend the complete requirements and scope.
The next step should be examining the details of the product and reasons for each metric, along with the issues that may crop up during the process. Every detail of your role contributes to a more robust system, so it’s important that you explain your role in an accurate manner.
Q2. Which data engineering tools did you use in a recent project?
Ans. This should be easier for you to answer. The interviewer wants to assess your knowledge about different tools and how you use them in different types of projects. Use your previous experience to tell them about the relevant tools used.
It can be a good idea to go through the tools the company you are interviewing for, is using. If you have worked on those tools, then use them in your answer.
You should also let the interviewer know about the reasons why you chose that particular tool for a particular project. Your reasoning will help them to understand your level of knowledge.
Q3. What is the difference between a database and a data warehouse?
Ans. The main difference between the database and the data warehouse is that a database is an organized collection of related data that stores the data in a tabular format, while the data warehouse is a central location that stores consolidated data from multiple databases.
Database management system or DBMS is software that allows users to create, manipulate and manage databases. The database helps to perform the basic functionalities of an organization. On the other hand, the data warehouse is a system for reporting and data analysis. It is the main component of business intelligence providing high performance for analytical queries. Typically, the business community uses data warehouses.
Q4. What is a database model?
Ans. A database model is a logical structure that the database adopts, including the relationships and constraints that determine how data is stored and organized, and how data is accessed. Likewise, a database model also defines what type of operations can be performed with the data, that is, it also determines how it is manipulated, also providing the basis on which the query language is designed.
Q5. What are the different types of database models?
Ans. There are different types of database models; you can just name all the models or explain them, if the interviewer asks you to. Below is the list of all types of database models.
Relational database model
The relational database model is used by relational databases and orders the data in tables (relationships) made up of columns and rows.
If we are going to use a hierarchical database, the data model that we will use will be the hierarchical one, which is characterized by presenting the data in an inverted tree structure, where each record has a single root node, from which other nodes arise.
The network database model starts from the hierarchical model, but here one-to-many or many-to-many relationships are allowed between linked records, having multiple parent records.
The object-oriented database model defines the database as a collection of objects used in object-oriented programming.
The object-relational model combines the relational and object-oriented database models in a hybrid model, so that it works in a similar way to the relational model, but incorporates functions of the object-oriented model.
The entity-relationship model is basically the previous step to a relational database model since it is a diagram made through some basic elements and their relationship between them.
Inverted file model
The inverted file model also called an inverted index contains data that is used as keys in a query table. The values in the table are used as pointers to the location of each instance.
In the flat database model, the data is structured in two dimensions, in which all the objects in a specific column have values of the same type and all the objects in the same row are related between them.
The multidimensional database model is intended for the creation of specific OLAP (online analytical processing) applications.
When the data does not fit into the format of tables, rows, and columns, but is organized by means of labels with which it can be grouped and created hierarchies, we are talking about semi-structuring data.
The context model can be used when you need to incorporate elements from other database models.
In the associative model, the data is divided into entity and association, so that an entity is everything that exists independently and an association is something that only exists in relation to something else.
NoSQL database models
NoSQL database models are non-tabular databases and they store data differently than relational database models.
Q6. How is data modeling different from class modeling?
Ans. Data modeling is all about exploring data structures. Data models can be used for a variety of purposes, from high-level conceptual models to physical data models. From an object-oriented developer’s point of view, data modeling is conceptually similar to class modeling.
Data modeling identifies entity types, while class modeling identifies classes. Data attributes are assigned to feature types in the same way that you would assign attributes and operations to classes.
Q7. What are the different configuration files in Hadoop?
Ans. Different configuration files in Hadoop are –
Q8. What are different types of structured data?
Ans. Below are the different types of structured data –
Created data – Created data are those generated by the company or organization itself to carry out its market analysis, for example – customer surveys.
Processed data – These are the data that is collected from the completion of completed transactions, for example, processing the online shopping cart.
Compiled data – Compiled data is collected from the general population as a whole, such as censuses, registered cars, educational level, etc.
Experimental data – This data is generated when different marketing actions are carried out as an experiment, to check which ones are most effective. They can also come from combining created and transactional data.
Q9. What are different types of unstructured data?
Ans. Unstructured data can be classified as follows:
Captured data – Captured data is the one that are passively generated by users, through their behavior, such as Google searches, GPS information or biometric information from smart bands, etc.
UGC – User-generated data is the data that users actively generate when browsing the Internet, including messages on social networks, comments on publications, videos on YouTube, etc.
Q10. Do you have any working knowledge of scripting languages like Python, Java, Bash, or others?
Ans. This is one of the most commonly asked data engineer interview questions. Since scripting languages play a crucial role in building effective, efficient data infrastructure and automating data flow, interviewers expect you to have a working knowledge of the languages.
Hope these data engineer interview questions helped you. We will keep updating this article with both basic and advanced data engineer interview questions.
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