Top 200 Data Engineer Interview Questions & Answers 2023

A data engineer is an IT professional whose primary responsibility is to prepare data for analytical or operational purposes. These software engineers are typically in charge of creating data pipelines that connect information from various source systems. They combine, consolidate, and cleanse data before structuring it for use in analytic applications. They want to make data more accessible and optimize their company’s big data ecosystem. Data engineering is a term used in the field of big data. It focuses on data collection and research applications. The data generated by various sources is simply raw data. Data engineering aids in the transformation of raw data into useful information. This article on Top 200 Data Engineer Interview Questions & Answers 2023 will help you with details on Essential Data Engineering Interview Questions.

Top 200 Data Engineer Interview Questions & Answers 2023

1. What is Data Engineering?

Ans : When working with data, the term “Data Engineering” is used. Data Engineering refers to the primary process of converting a raw entity of data into useful information that can be used for a variety of purposes. This requires the Data Engineer to work with data by collecting data and conducting research on it.

2. Define Data Modelling.

Ans : Data modelling is the process of simplifying complex software designs by breaking them down into simple diagrams that are easy to understand, and it has no prerequisites. There is a simple visual representation between the data objects involved and the rules associated with them, which provides numerous benefits.

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3. In a nutshell, what is Hadoop?

Hadoop is an open-source framework for data manipulation and storage, as well as the execution of applications on clusters. Hadoop has long been the gold standard for working with and handling Big Data. The main advantage is the simple provision of massive amounts of storage space and massive amounts of processing power to handle limitless data.

4. What is NameMode in HDFS?

NameNode is an essential component of HDFS. It is used to store all of the HDFS data while also keeping track of the files in all clusters. However, you should be aware that the data is stored in the DataNodes rather than the NameNodes.

5. What is Hadoop Streaming?

Hadoop streaming is one of Hadoop’s most popular utilities, allowing users to quickly create maps and perform reduction operations. This can then be submitted to a specific cluster for use.

6. What is Star Schema?

The star schema, also known as the star join schema, is one of the simplest schema in the Data Warehousing concept. Its structure is shaped like a star and is made up of fact tables and associated dimension tables. When dealing with large amounts of data, the star schema is commonly used.

7. What exactly is the distinction between a Data Architect and a Data Engineer?

A Data Architect is someone who is in charge of managing the data that comes into the organization from various sources. A Data Architect must have data handling skills, such as database technologies. The Data Architect is also concerned about how changes in data will cause major conflicts in the organization model. A Data Engineer is now primarily responsible for assisting the Data Architect in the setup and establishment of the Data Warehousing pipeline and the architecture of enterprise data hubs.

8. What is Rack Awareness?

Rack awareness is a concept in which the NameNode uses the DataNodes to increase incoming network traffic while simultaneously reading or writing to the file that is closest to the rack from which the request was made.

9. What role does Hive play in the Hadoop ecosystem?

Hive is used to provide the user interface for managing all of Hadoop’s stored data. The data is mapped using HBase tables and is worked on as needed. Hive queries (similar to SQL queries) are run to generate Map Reduce jobs. This is done to keep the complexity under control when running multiple jobs at the same time.

10. What is SerDe in Hive?

In Hive, SerDe stands for Serialization and De-serialization. It is the operation that occurs when records are passed through Hive tables. The De serialize takes a record and converts it into a Java object that Hive can understand. The Serialize now takes this Java object and converts it into a format that HDFS can process. HDFS eventually takes over the storage function.

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You should be much more confident in your interview preparation for that position now that you are well-versed in the data engineer interview questions and the most important things to remember about the interview process itself.

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