Data engineer interview questions

In the digital world, technology constantly and consistently evolving. Here, data is the foundation for making relevant decisions across various sectors. Since companies seek better ways to use data, there has been a greater need for highly qualified specialists who can develop and implement a comprehensive system of solid infrastructure. 

Data engineers are involved in establishing and managing the systems architecture that allows organizations to transform raw information into meaningful insights. As big data, cloud computing, and advanced analytics continue to increase across industries such as finance, health care, e-commerce, etc., the demand for skilled data engineers has also increased tremendously.

For this reason, an interview process while hiring data engineers is essential. Due to the technical nature of this role, you must evaluate a candidate’s knowledge and understanding, including programming skills, database management, data modeling, system architecture, etc. Further, assessing problem-solving skills, communication, and teamwork performance with cross-functional groups is critical. Let’s look at the common interview questions for data engineers.

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Fundamental technical questions

Below are some technical queries in Data Engineer job interviews.

1. What is the difference between an SQL INNER JOIN and a LEFT JOIN? 

INNER JOIN: It retrieves records whose values match in both tables. It yields only the standard rows between these tables.

Example:

SELECT employees.employee_id, employees.employee_name, departments.department_name

FROM employees

INNER JOIN departments de on e.department_id = de.department_id;

LEFT JOIN: It gets all records from the left table and those that match on the right. If there is no coincidence, NULL values are given to the table’s columns.

Example:

SELECT employees.employee_id, employees.employee_name, departments.department_name

FROM employees

LEFT JOIN departments using (employees.department_id = departments.department_id);

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2. What is normalization, and why should it be important in database design?

Normalization helps set up data in a database, hence minimizing redundancy and dependency. Reducing data redundancy and the likelihood that an insert, update, or delete anomaly occurs is critical.

3. Detail how a primary key differs from a secondary key.

The primary key identifies every record in a table and guarantees no duplicate values within that column.

Example:

CREATE TABLE students (

student_id INT PRIMARY KEY,

student_name VARCHAR(50)

);

A foreign or secondary key refers to another table’s primary key that creates a relationship between two tables.

Example:

CREATE TABLE grades (

    grade_id INT PRIMARY KEY,

    student_id INT,

    grade VARCHAR(2),

    FOREIGN KEY (student_id) REFERENCES students(student_id)

4. What is denormalization, and in what scenarios would you consider using it?

It is all about intentionally introducing redundancy into a table by combining or duplicating data.

Scenarios for using denormalization:

• Read-heavy applications where performance is critical
• Situations where frequent complex joins can be avoided

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5. What are the ACID properties in database transactions? Explain each property.

• Atomicity guarantees that the transaction is viewed as a unitary entity. All the changes in a transaction or none are committed.
• Consistency ensures that a transaction takes the database from one valid state to another. The database stays consistent before and after the transaction in a steady state.
• Isolation ensures that the execution of a transaction is isolated from the effects of other transactions. 
• Durability guarantees that once a transaction is committed, its effects will persist and survive any subsequent failures, such as power outages or system crashes.

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Big Data Technologies

1. Explain the key components of the Hadoop ecosystem and their roles in distributed computing.

The Hadoop ecosystem includes the Hadoop Distributed File System (HDFS) for storage, MapReduce for processing, and others like YARN, Hive, and Pig. They are essential for distributed storage, breaking large files into blocks across the cluster.

2. What is the role of YARN in the Hadoop ecosystem, and how does it facilitate resource management?

YARN (Yet Another Resource Negotiator) is the resource manager in Hadoop. It is responsible for managing and allocating resources to applications. YARN enables multiple applications to share resources on a Hadoop cluster.

3. Differentiate between the batch processing model of MapReduce and the interactive processing model of Apache Spark.

MapReduce handles data using divisions and conquests to formulate Map (divide) and Reduce. It is batch-processable. Contrastingly, Apache Spark caters to both batch and intermediate processing by relying on in-memory computing.

4. How does data partitioning in Hadoop contribute to parallel processing?

Data partitioning refers to the situation in which data is split into small pieces and processed in parallel on different nodes. It fosters better parallel processing as each node manages its own data set.

5. How does Hadoop handle fault tolerance, and what is the significance of data replication in HDFS?

The haploid is where fault tolerance in HDFS occurs through replication. With data replication, there is a copy of the data that can be accessed from another node if one fails.

Job Roles	Salary Packages (INR)
Data Analyst	INR 4.11 LPA
Data Engineer	INR 8.69 LPA
Data Architect	INR 19.31 LPA
Machine Learning Engineer	INR 9.58 LPA
Data Scientist	INR 7.07 LPA

Payscale

Become a Data Engineer with MAHE

The MAHE MSc-Data Science is a 24-month online course that prepares professionals with the necessary occupational requisites of a data science career. The curriculum is designed for machine learning, big data analytics, stats, visualizations, and perfect basic data science knowledge.

The online mode enables individuals to undertake the program without necessarily deterring from their work schedules or other obligations.

Conclusion

In conclusion, MAHE’s MSc-Data Science program is a complete and customizable choice for people who want to become data engineers. The combination of theoretical knowledge and essential skills assessed in Data Engineer interview skills is a perfect preparation for the dynamic field.