Data Engineering Most Essential Interview Questions
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Introduction
Data engineering is the field of study that deals with the design, construction, deployment, and maintenance of data processing systems. The goal of this domain is to collect, store, and process data efficiently and efficiently so that it can be used to support business decisions and power data-driven applications. This includes designing and implementing data pipelines, building data storage solutions, and building data processing systems to process big data. Data engineers work closely with data scientists, analysts, and stakeholders to ensure that data systems meet organizational needs and support the generation of valuable insights.
Today’s article will cover questions and topics relevant to data engineering that you might expect to come across in your following interview. Learning objectives for today would be
- To understand the nuances of data engineering
- The features and functioning of Hadoop
- Understanding MapReduce in Hadoop
- Understanding the Snowflake Schema
- The difference between structured and unstructured data and how to convert from one to another
This article was published as a part of the Data Science Blogathon.
Table of Contents
- Data Engineering Beginner-Level Interview Questions
1.1. What is data engineering?
1.2. What is the difference between structured and unstructured data?
1.3. What are the features of Hadoop?
1.4. What are the various aspects of Hadoop? - Data Engineering Intermediate-Level Interview Questions
2.1. Explain MapReduce in Hadoop.
2.2. What is Name Node? How does a Name Node communicate with Data Node?
2.3. Explain the Snowflake Schema in brief.
2.4. What is Hadoop Streaming?
2.5. What are the Skewed Table and SerDe in the Hive? - Data Engineering Expert-Level Interview Questions
3.1. What is orchestration?
3.2. What are the different data validation approaches?
3.3. Explain the use of the Hive in the Hadoop ecosystem.
3.4. How does a data warehouse differ from an operational database?
3.5. How to reform unstructured data into structured data?
3.6. What is the difference between Data Architect and Data Engineer? - Conclusion
Data Engineering Beginner-Level Interview Questions
Q1. What is Data Engineering?
Data Engineering is designing, constructing, and maintaining the architecture and infrastructure for storing, processing, and analyzing large and complex data sets to support data-driven decision-making. It involves using various tools, technologies, and techniques to manage data, ensure data quality and integrity, and make data available for analysis and visualization. Data engineering is a crucial aspect of the data science workflow and provides the foundation for data-driven insights and discoveries.
Q2. What is the Difference Between Structured and Unstructured Data?
Data in the real world comes mainly in two different forms – structured and unstructured data. Structured data refers to those data that possess a definite format – often arranged in tabular format with names and values distinctly mentioned in it. Examples of the same include data or spreadsheets, CSVs, etc. However, most real-world data is unstructured, meaning they do not possess a pre-defined structure or organization. Examples include text, audio, video, or image data. Structural data is easier to process using computational tools, while unstructured data requires complex processes like NLP, text mining, or image processing to make sense out of the data. Thus there is a constant attempt to transform unstructured data into structured data, as w will see in the proceeding questions and concepts.
Q3. What are the Features of Hadoop?
Q4. What are the Components of Hadoop?
Data Engineering Intermediate-Level Interview Questions
Q1. Explain MapReduce in Hadoop.
Q2. What is Name Node? How Does a Name Node Communicate with Data Node?
Q3. Explain the Snowflake Schema in Brief.
Q4. What is Hadoop Streaming?
Q5. What are the Skewed Table and SerDe in the Hive?
Data Engineering Expert-Level Interview Questions
Q1. What is Orchestration?
Q2. What are the Different Data Validation Approaches?
Q3. Explain the Use of the Hive in the Hadoop Ecosystem.
Q4. How does a Data Warehouse Differ from an Operational Aatabase?
Q5. How to Transform Unstructured Data into Structured Data?
Q6. What is the Difference Between Data Architects and Data Engineers?
Conclusion
Well, I hope you were able to understand today’s reading! If you were able to answer all the questions, then bravo! You are on the right track toward your preparation; if not, there’s no need to be concerned. The real value of today’s blog would come up if you can absorb these concepts and then apply them to the questions you would be facing in your interviews.
To summarize for you, the key takeaways of today’s articles would be:
- To understand data engineering and the difference between structured and unstructured data
- The features and components of Hadoop
- MapReduce in Hadoop
- The Snowflake Schema
- Hadoop Streaming
- Skewed Table and SerDe in Hadoop
- The concept of orchestration
- The different approaches to data validation
- The use of Hive in Hadoop
- The differences between a data warehouse and an operational database
- The process of transforming unstructured data into structured data
- And finally, the difference between a data engineer and a data architect
If you go through these thoroughly, I can ensure that you have covered the length and breadth of data engineering. The next time you face similar questions, you can confidently answer them! I hope you found this blog helpful and that I successfully added value to your knowledge. Good luck with your interview preparation process and your future endeavors!
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