Data Engineer Interview Questions

Data Engineer Interview Questions and Answers

The data engineering field is one of the systems which help us to collect data from different resources. This is a broad-level application that is used in almost all companies. We know companies can collect a huge amount of data and they need talented people to process that data or we can say that analysts collected data. Today we have a lot of opportunities in data science as well as the data engineering field.

Top 15 Data Engineer Interview Questions and Answers

Below are the top questions and answers of the Data Engineer. These questions are helpful while giving mock tests or interviews.

Q1. Can you explain what data engineering is?

Answer:

It is nothing but one type of application which is used to collect the data from the different resources and analyze it according to the requirement.

Q2. Could you explain the distinction between the warehouse and the operational database?

Answer:

Warehouse data is used for data analysis of raw data, while operational data is used for transaction processing with a lot of data. We have functions in operational data and subject-oriented data in warehouse data. The most significant distinction is that warehouse query performance is high while operational data performance is low.

Q3. Do you know the schema names which are available in modeling?

Answer:

Normally there are two types of schema such as Star Schema and Snowflake schema.

Q4. Can you explain the difference between data scientists and data engineering?

Answer:

Data scientists can work in companies, government, and applied sciences, among other areas. The same goal is shared by all data scientists: to discover insights from data that are pertinent to their work area. Data engineers develop or integrate numerous components of intricate systems, taking into account the requirements of the end product, the company’s objectives, and the required information. Because of this, extremely complicated data pipelines must be built.

Q5. Can you explain the repercussions of the NameNode crash?

Answer:

We know that in the HDFS cluster, there is a single NameNode that is used to keep track of another node, the DataNode, due to the single point of failure. If the NameNode fails, the system is unable to access.

Q6. What is a star schema?

Answer:

A star schema can have one fact table and a number of related dimension tables in the center of a data warehouse. Because of how similar its structure is to that of a star, it is known as a star schema. Normally the star schema is a very simple part of the warehouse. The Star Join Schema is another name for it, and it is made to work with huge data sets.

Q7. Can you explain the replication factor in data engineering?

Answer:

The number of times that each Data Block is replicated by the Hadoop framework is called the replication factor. Replicating the block provides fault tolerance. The default setting for the replication factor is 3, but it can be changed to 2 (less than 3) or raised to meet your requirements (more than 3).

Q8. What is orchestration?

Answer:

Normally, we know IT companies require multiple servers as well as different applications so we need to maintain their stability of them through manual work but it is not scalable. During maintenance, it is a more challenging task to manage them. So we need to automate this task to avoid manual interpretation, so we can easily configure the jobs. Orchestration comes in handy in this situation. Basically, orchestration is used for the configuration as well as different management and it is automated. Orchestration makes it easier for IT to manage complicated processes and workflows. Kubernetes and OpenShift are just two of the many container orchestration platforms available.

Q9. Do you have any idea about Apache Spark?

Answer:

Apache Spark is an open-source tool used for distributed processing of data. It makes use of efficient query execution and in-memory caching for quick queries against any size of data. Simply put, Spark is a fast and flexible general-purpose data processing engine.

Q10. Can you explain the difference between Spark and MapReduce?

Answer:

Spark is an enhancement to Hadoop. Consequently, for lighter workloads, Spark’s data processing speed is up to 100 times faster than MapReduce. In contrast to MapReduce’s two-stage execution method, Spark also creates a Directed Acyclic Graph (DAG) to schedule tasks and coordinate nodes in the Hadoop cluster.

Q11. What is a Skewed table?

Answer:

Skewed is one type of table that contains the column which occurs frequently. Distribution in the table is nothing but the result and one more important thing is when we are trying to create a skewed table in the hive the value of skewed is placed in a separate file. The remaining information is stored in another file.

Q12. How many types of table creation functions are available in the hive?

Answer:

Basically there are four functions such as Explode (array), Explode (map), JSON_truple(), and Stack().

Q13. Why we use *args and **kwargs?

Answer:

When we need to use the order function on the command line then we can use *arg and when we need to use an unordered group of arguments that we need to pass to the function then we can use **kwargs.

Q14. Do you know the spark execution plan?

Answer:

Statement in SQL (SQL, Spark SQL, Dataframe operations, and so on) is turned into a set of logical and physical processes that are optimized by an execution plan. The SQL (or Spark SQL) statement will perform a series of operations on the DAG (Directed Acyclic Graph), which will then be sent to Spark Executors.

Q15. What are the four Vs in data engineering?

Answer:

Volume, Veracity, Velocity, and Variety.

Conclusion

From this article, we are able to understand the Data engineer Interview Question. It provides the basic idea about the mid-level and higher-level concepts of data engineering. Data engineer Interview Question is a key point for every interview or we can say that every technology.