Last Updated on April 23, 2023 by Akwaowo Akpan
This article is on data engineer interview questions and answers. Data engineering is the process of designing and building large-scale data gathering, storage, and analysis systems. It’s a broad field with applications in virtually every business.
It’s a multidisciplinary subject that entails working with data scientists, analysts, and software developers to define the data pipeline. Data engineers design systems for collecting, processing, and transforming raw data into information that data scientists and business analysts can interpret. Given the ever-increasing reliance on large volumes of data, data engineers appear to have a bright future. Companies use the data they collect to help them grow their businesses, thus skilled data engineers will always be in demand. Finding the right engineer for data roles is exceedingly difficult, and competition for those positions can be strong. Let’s look at data engineer questions and answers
Data Engineer Interview Questions and Answers for Freshers
1. What is Data Engineering?
2. What is Data Modeling?
3. What are the design schemas available in data modeling?
- Star Schema
- Snowflake Schema
4. What is the difference between a data engineer and a data scientist?
- Data science is a broad topic of research. It focuses on extracting data from extremely huge datasets (sometimes it is known as “big data”). Data scientists can operate in a variety of fields, including industry, government, and applied sciences. All data scientists have the same goal: to analyze data and derive insights from it that are relevant to their field of work.
- A data engineer’s job is to develop or integrate many components of complex systems, taking into account the information needed, the company’s goals, and the end requirements. This necessitates the creation of extremely complicated data pipelines. These data pipelines, like oil pipelines, take raw, unstructured data from a variety of sources. They then channel them into a single database (or larger structure) for storage.
5. What are the differences between structured and unstructured data?
On the basis of | Structured | Unstructured |
---|---|---|
Storage | Structured data is stored in DBMS. | It is stored in unmanaged file structures. |
Flexibility | It is less flexible as it is dependent on the schema. | It is more flexible. |
Scalability | Not easy to scale. | Easy to scale. |
Performance | Since we can perform a structured query, the performance is high. | The performance of unstructured data is low. |
Analysis factor | Easy to analyze. | Hard to analyze. |
6. What are the features of Hadoop?
- It is open-source and easy to use.
- Hadoop is extremely scalable. A significant volume of data is split across several devices in a cluster and processed in parallel. According to the needs of the hour, the number of these devices or nodes can be increased or decreased.
- Data in Hadoop is copied across multiple DataNodes in a Hadoop cluster, ensuring data availability even if one of your systems fails.
- Hadoop is built in such a way that it can efficiently handle any type of dataset, including structured (MySQL Data), semi-structured (XML, JSON), and unstructured (Images and Videos). This means it can analyze any type of data regardless of its form, making it extremely flexible.
- Hadoop provides faster data processing. More Features.
7. Which frameworks and applications are important for data engineers?
8. What is HDFS?
9. What is a NameNode?
10. What are the repercussions of the NameNode crash?
11. What is a block and block scanner in HDFS?
- Block: In HDFS, a “block” refers to the smallest amount of data that may be read or written.
- Block Scanner: Block Scanner keeps track of the list of blocks on a DataNode and checks them for checksum problems. To save disc bandwidth on the data node, Block Scanners use a throttling technique.
12. What are the components of Hadoop?
- Hadoop Common: A collection of Hadoop tools and libraries.
- Hadoop HDFS: Hadoop’s storage unit is the Hadoop Distributed File System (HDFS). HDFS stores data in a distributed fashion. HDFS is made up of two parts: a name node and a data node. While there is only one name node, numerous data nodes are possible.
- Hadoop MapReduce: Hadoop’s processing unit is MapReduce. The processing is done on the slave nodes in the MapReduce technique, and the final result is delivered to the master node.
- Hadoop YARN: Hadoop’s YARN is an acronym for Yet Another Resource Negotiator. It is Hadoop’s resource management unit, and it is included in Hadoop version 2 as a component. It’s in charge of managing cluster resources to avoid overloading a single machine.
13. Explain MapReduce in Hadoop.
14. What is the Heartbeat in Hadoop?
![53 Data Engineer Interview Questions and Answers 3 heartbeat in Hadoop](https://d3n0h9tb65y8q.cloudfront.net/public_assets/assets/000/003/043/original/heartbeat_in_Hadoop.png?1648818449)
The heartbeat is a communication link that runs between the Namenode and the Datanode. It’s the signal that the Datanode sends to the Namenode at regular intervals. If a Datanode in HDFS fails to send a heartbeat to Namenode after 10 minutes, Namenode assumes the Datanode is unavailable.
15. How does the NameNode communicate with the DataNode?
- Block reports
- Heartbeats
16. What happens when the block scanner detects a corrupt data block?
- First and foremost, when the Block Scanner detects a corrupted data block, DataNode notifies NameNode.
- NameNode begins the process of constructing a new replica from a corrupted block replica.
- The replication factor is compared to the replication count of the right replicas. The faulty data block will not be removed if a match is detected.
17. Explain indexing.
18. Explain the main methods of reducer.
- setup(): This command is used to specify parameters such as the size of input data and the distributed cache.
- cleaning(): is a function for deleting temporary files.
- reduce(): it’s called once per key with the corresponding reduced task.
19. What is COSHH?
20. What is the relevance of Apache Hadoop’s Distributed Cache?
21. What are the four Vs of Big Data?
- Volume
- Veracity
- Velocity
- Variety
22. Explain the Star Schema in Brief.
23. Explain the Snowflake Schema in Brief.
![53 Data Engineer Interview Questions and Answers 4 Snowflake Schema](https://d3n0h9tb65y8q.cloudfront.net/public_assets/assets/000/003/046/original/Snowflake_Schema.png?1648818591)
A snowflake schema is a logical arrangement of tables in a multidimensional database that matches the snowflake shape (in the ER diagram). A Snowflake Schema is an enlarged Star Schema with additional dimensions. After the dimension tables have been normalized, the data is separated into new tables.
Snowflaking has the potential to improve the performance of certain queries. The schema is organized so that each fact is surrounded by its related dimensions, and those dimensions are linked to other dimensions, forming a snowflake pattern.
24. Name the XML configuration files present in Hadoop.
- Core-site
- Mapred-site
- Yarn-site
- HDFS-site
25. What is Hadoop Streaming?
26. What is the Replication factor?
27. What is the difference between HDFS block and InputSplit?
Block | InputSplit |
---|---|
In Hadoop, a block is the physical representation of data. | InputSplit is the logical representation of data in a block. It is primarily used in the MapReduce program or other data processing techniques. |
The HDFS block size is set to 128MB by default, but you can modify it to suit your needs. Except for the last block, which can be the same size or less, all HDFS blocks are the same size. | By default, the InputSplit size is nearly equal to the block size. |
28. What is Apache Spark?
29. What is the difference between Spark and MapReduce?
Data Engineer Interview Questions for Experienced
30. What are Skewed tables in Hive?
![53 Data Engineer Interview Questions and Answers 5 hive](https://d3n0h9tb65y8q.cloudfront.net/public_assets/assets/000/003/047/original/hive.png?1648818637)
31. What is SerDe in the hive?
32. What are the table creation functions in Hive?
- Explode(array)
- Explode(map)
- JSON_tuple()
- Stack()
33. What are *args and **kwargs used for?
34. What do you mean by spark execution plan?
35. What is executor memory in spark?
36. Explain how columnar storage increases query speed.
![53 Data Engineer Interview Questions and Answers 6 how columnar storage increases query speed](https://d3n0h9tb65y8q.cloudfront.net/public_assets/assets/000/003/048/original/how_columnar_storage_increases_query_speed.png?1648818664)
37. What is schema evolution?
38. What do you mean by data pipeline?
![53 Data Engineer Interview Questions and Answers 7 data pipeline](https://d3n0h9tb65y8q.cloudfront.net/public_assets/assets/000/003/049/original/data_pipeline.png?1648818695)
39. What is orchestration?
40. What are different data validation approaches?
- Data type check: It confirms that the data entered is of the correct data type.
- Code check: A code check verifies that a field is chosen from a legitimate list of options or that it corresponds to specific formatting constraints. Checking a postal code against a list of valid codes, for example, makes it easier to verify if it is valid.
- Range check: It ensures that input falls in a predefined range.
- Format check: Many data types follow a predefined format. Format check confirms that. For example, a date has formats like DD-MM-YY or MM-DD-YY.
- Consistency check: It confirms that the data entered is logically correct.
- Uniqueness check: It ensures that the same data is not entered multiple times.
41. What was the algorithm you used in a recent project?
- Why did you choose this algorithm?
- What is the scalability of your model?
- If you were given more time, what could you improve?
43. Why are you applying for the Data Engineer role in our company?
44. What tools did you use in your recent projects?
45. What challenges did you face in your recent project and how did you overcome them?
46. Which Python libraries would you recommend for effective data processing?
47. How do you handle duplicate data points in a SQL query?
48. Have you ever worked with big data in a cloud computing environment?
- Its flexibility and scalability.
- Security and mobility.
- Risk-free data access from anywhere.
Conclusion
Data Engineering is a demanding career and it takes a lot of effort to become one. As a data engineer, you must be prepared for data science challenges that may arise during an interview. Many problems have multi-step solutions, and having them planned ahead of time allows you to map out solutions as you go through the interview process. Here, you will not only get information about commonly asked interview questions on data engineering, but you will also ace the interview with your responses.