How To Prepare For Data Engineering With Python Interview 

Data Engineering is a wide discipline that largely finds its applications in big data and focuses on data collection and research applications. The main purpose of Data Engineering activity is to ensure a consistent flow of data that enables data-driven decision-making in an organization.

Data engineering is the field of raw data study followed by transforming, profiling, cleansing, and aggregating these massive datasets into relevant or useful information. This information or data flow can then be accomplished in several diverse ways, and one of them is using Python-one of the most popular programming languages created by Guido van Rossum. 

Python is popular in data engineering because of its simple syntax, strong typing, and wide range of third-party libraries. These include SciPy, TensorFlow, Pandas, SQLAlchemy, and NumPy used across different industries. Python for Data Engineering leverages all the features of Python and fine-tunes it accordingly for all your business-specific data engineering needs. 

Today we will cover everything from the scope and future of Data Engineering with Python to the most important data engineer interview questions and more.

Scope And Future of Data Engineering 

The last few years have seen the demand for data engineering professionals rising exponentially. From data scientists and machine learning engineers to big data engineers, there has been no shortage of top positions to help qualified candidates build high-salary careers working with big data.

Overall, the data engineering and data analytics space has an exciting future. Unlike earlier, when the companies were largely focused on collecting and simply visualizing data, they have now started to think about better and more innovative ways to transform, manage and track their datasets. 

This promising next phase in data engineering requires organizations to step back and redefine their goals and unique needs. To navigate this successfully, flexibility, efficiency, and accessibility are the key pillars today driving most of the professionally qualified data engineers interviewed.

Data Engineer Python Interview Questions You Need to Know 

If you wish to build a successful career in the field of data engineering and enroll in a Python Data Engineering course, here are some of the important Python Data Engineer interview questions you need to prepare-

1. What Is the Difference Between Relational and Non-relational Databases?

Relational and non-relational databases refer to two different management systems that allow us to create databases that will help efficiently manage complex datasets.

A relational database here is a database management system in which data is primarily stored in distinct tables from where they can be easily accessed or reassembled in diverse ways under user-defined relational tables. 

Examples of Relational Databases-Oracle and MySQL.

A non-relational database, on the contrary, a database is not built around tables. This type of database contains data in forms or a large amount of data in an unstructured or semi-structured form.

Examples of Non-Relational Databases- Apache Cassandra, MongoDB

2. Define SQL Aggregation Function.

In database management, an aggregate function refers to a function where the values of several rows are grouped as input on certain criteria to form one single value of more significant meaning.

Put, an aggregate function in SQL performs a calculation on different values and returns a single value. Also, except for COUNT(*), SQL aggregate functions ignore null values. Aggregate functions here are often used with the GROUP BY clause of the SELECT statement.

Among some of the SQL aggregate functions are:

1. Count()
2. Sum()
3. Min()
4. Max()
5. Avg()

In general, we use SQL aggregate functions as expressions only in the below situations-

• The select list of a SELECT statement (either a subquery or an outer query).
• A HAVING clause.

3. Explain Ways to Speed Up SQL Queries.

Speeding up SQL queries is important to ensure that our websites work much faster to deliver a satisfactory customer experience. Some ways to speed up SQL database queries include-

• Avoid nested queries/views– Whenever you nest a query/view inside another query/view, the result is many data returns for each query, leading to a slowdown of your query. 
• Ensure to use column names instead of SELECT*– When using SELECT statements, use only the columns you require in your result, and not SELECT * from … This will help you reduce the result size considerably and speed up your SQL query.
• Use CASE instead of UPDATE- Since the UPDATE statement generally takes much longer than the CASE statement due to logging, it is best to use the CASE statement as it helps you determine what needs to be updated and, in turn, makes your SQL queries faster.

4. Explain NoSQL and Their Use Cases.

NoSQL databases (or “not only SQL” databases) refer to the non-tabular databases which store data differently than relational tables. Typically, NoSQL databases come in a range of diverse types based on their data model. 

The main types of NoSQL are-

• Document
• Graph 
• key-value 
• Wide-column 

They offer flexible schemas and scale easily with massive amounts of data and high user loads.

Among the top use cases of NoSQL databases are- 

• Storage of both structured and semi-structured data
• Requirements for scale-out architecture
• Various modern application paradigms, such as microservices and real-time streaming

5. What Is the Difference Between NoSQL and SQL?

The key differences between the two types are:

1. SQL or Relational Databases (RDBMS)databases are largely table-based. At the same time, NoSQL or distributed databases are document-based, graph databases, key-value pairs, or wide-column stores. 
2. SQL databases are vertically scalable, meaning that the load on a single server can be increased using RAM or CPU. On the other hand, NoSQL databases are horizontally scalable, meaning you can handle more traffic by adding more servers to your NoSQL database.
3. What Is Database Caching, and How to Use Them?

Database caching is a buffering method that stores frequently queried data by users in temporary memory. It, in turn, makes data easier to access and reduces workloads for databases. 

The database cache can be set up either in different tiers or on its own, based on the use case. It works with almost any type of database, including but not limited to:

• Relational databases such as Amazon RDS.
• NoSQL databases include Amazon DynamoDB, MongoDB, Apache Cassandra, and Azure Cosmos DB.

7. What Is ETL, and What Are the Common Aspects of the ETL Process and Big Data Workflows?

ETL (Extract, Transform, Load) refers to extracting data from various data sources and moving the same to a central host, which is optimized for data analytics. At its core, the ETL process encompasses data extraction, transformation, and loading. 

ETL in organizations offers the foundation for data analytics and machine learning workstreams. Using several rules, ETL cleanses and organizes data to address unique business intelligence needs such as monthly reporting, backend processes, or end-user experiences. 

8. Talk About Your Experience with NoSQL Databases. When Is It Better to Build a NoSQL Database Than a Relational Database?

Since databases have pros and cons, data engineers should be able to explain them. In terms of NoSQL databases, they offer an excellent way to store and retrieve data modeled in means other than the tabular relations used in relational databases. For instance, NoSQL databases can be a good option when an organization requires scale.

Now that you know the most important Data Engineering in Python questions let’s discuss a data engineer’s key roles and responsibilities in the next section.

Roles And Responsibilities of A Python Data Engineer 

Data engineers in any organization work in various settings to build systems that collect, organize, manage, and convert raw data into relevant, usable information for business analysts and data scientists to interpret. 

The key goal of a data engineer is to make data accessible so that organizations can use it to both evaluate and optimize their performance. Some of the roles and responsibilities of a Python data engineer when working with data include the following-

• Acquire different datasets that align with business needs.
• Consistently works in building, testing, and maintaining various database architectures.
• Develop algorithms to convert data into relevant and useful information.

• Ensure complete compliance with data governance as well as data security policies.

How Does Python Help Data Engineering?

Python is one of the most popular programming languages today. With numerous applications in various fields, Python is best suited for deployment, analysis, and maintenance because of its flexible and dynamic nature. 

Python for Data Engineering is considered a crucial skill required in the field to set up statistical models and process data effectively. Besides this, Python helps data engineers build robust and efficient data pipelines, as several data engineering tools use Python in the backend.

The Data Engineering Industry in The Indian Context 

Data engineering is a huge market in India today, with approximately USD 18.2 billion in size in 2022. This is expected to grow in the next five years to reach an impressive USD 86.9 billion by 2027.

Data engineers in India work across various sectors, experience levels, and cities. In terms of salary, data engineers on the Internet/ sector command the highest median salary of approximately INR 28.5 lakhs per annum. It is important to note that the higher demand for the role of data engineers among companies and the low supply are one of the key reasons for the high salaries of data engineers in the industry. 

Research also suggests that the median salary for data engineers is INR 17.0 lakhs per annum as compared to the median salary of INR 16.8 lakhs per annum of all analytics professionals in India.

Data Engineering roles in India typically require a basic engineering degree with specialized Data Science skills. What is noteworthy here is that the backgrounds less strictly related to computer science, statisticians, physicists, and econometricians also often make excellent data engineers.

To Conclude 

The amount of data that’s being generated every day now has been nothing short of phenomenal. Further, it is also estimated that by 2025, the world will have created and stored a whopping 200 Zettabytes of data. 

While storing such a massive amount of data is a challenge in itself, what is even more difficult is to derive value from this amount of data, and this is where Data Engineering with Python can be of immense help. In this article, we have explored the significance of Python for Data Engineering, why you should learn Python for Data Engineering, and its critical role. 

We have covered the most important data engineer interview question and use cases of Python for data engineering. Overall, Python for data engineering is an important concept that plays a significant role in any organization.

So, as long as we keep generating data, data engineers will be in demand. A report in 2019 also indicates that Data Engineering is a top trending job in the overall technology industry, leaving behind Web Designers, Database Architects, and Computer Scientists.

If you wish to learn more about Data Engineering with Python, Hero Vired-a premium LearnTech company- offers various Python Data Engineering course programs in partnership with leading institutions to help you master the basics of Data Engineering with Python. Contact us to know more.