Hadoop Interview Questions & Answers!!! Do You Feel Ready to Ace Hadoop Interviews and Get Your Dream Job?

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Hadoop Interview Questions & Answers: 

1. What is Big Data?

Big Data is a term used to describe the large volume of structured and unstructured data generated by various technologies such as IoT devices, mobile phones, autonomous devices like robotics, drones, vehicles, and appliances. It is so vast and complex that traditional data management tools cannot store or process it efficiently.

2. What are the challenges and opportunities of Big Data?

The challenges of Big Data include the HUDO problem, HDFS, MapReduce, and other key components of the HUDO ecosystem. The opportunities of Big Data include the growing increase in the volume of data, which will lead to the demand for data management experts to manage and process such huge amounts of data. This will help data scientists and analysts draw high salaries.

3. What are some of the advanced topics in Big Data?

Some of the advanced topics in Big Data include the scope, Flow, Big, High, HBase, Oozi, popular HUDO projects, career opportunities in the Big Data domain, and tips to prepare for Big Data HUDO into requesters.

4. What are some of the key components of the HUDO ecosystem?

Some of the key components of the HUDO ecosystem include HDFS, MapReduce, and other advanced tools used for managing and processing Big Dat

5. What is HDFS?

HDFS stands for Hadoop Distributed File System, which is a distributed file system used for managing and storing large amounts of data in a distributed manner.

6. What is MapReduce?

MapReduce is a programming model and software framework used for processing large amounts of data in parallel. It is used for tasks such as data filtering, aggregation, and sorting.

7. What is the growing increase in the volume of data?

The growing increase in the volume of data refers to the exponential growth in the amount of data generated by various technologies such as IoT devices, mobile phones, autonomous devices like robotics, drones, vehicles, and appliances.

8. What is the demand for data management experts?

With the growing increase in the volume of data, the demand for data management experts will also increase. They will be responsible for managing and processing huge amounts of data efficiently, which will help data scientists and analysts draw high salaries.

9. What is the HBase database?

HBase is a distributed, NoSQL database built on top of the HDFS file system. It is used for managing and processing large amounts of data in real-time.

10. What are some of the popular HUDO projects?

Some of the popular HUDO projects include Apache Spark, Apache Hadoop& Apache Flink,

11. What is the form of data in the evolution of big data?

Data is generated in various formats, including unstructured videos and images.

12. What are the driving factors for the evolution of big data?

Big data refers to large, complex data sets that are difficult to process using traditional database system tools or traditional data processing applications. Traditional systems are often too old-fashioned to handle this data, making it difficult to understand and analyze

13. What are the challenges associated with data generation and storage?

As we continue to connect our devices with the internet and develop smarter devices, we must address the challenges associated with data generation and storage. By understanding the proper definition of big data and its challenges, we can better navigate the challenges and opportunities presented by the digital age.

14. What is the challenge associated with processing the vast amount of data generated since the invention of traditional systems?

The challenge lies in processing the vast amount of data that has been generated since the invention of traditional systems. This massive amount of data is coming from multiple sources, and it is difficult to classify and distinguish between big data and non-big data.

15. What are the fourV’s used to understand the challenges associated with big data?

The five V’s used to understand the challenges associated with big data are volume, variety, velocity, and value.

16. What are the three forms of data classified by the first V?

The three forms of data classified by the first V are structured format (tables), unstructured format (JSON, XML, and CSV files), and unstructured format (blog files, audio files, videos, and images).

17. How is velocity defined in the context of big data?

Velocity refers to the speed at which data accumulates. As computers evolved and the internet became more popular, web applications and the internet grew, leading to an increase in users, appliances, and apps.

18. What is big data analytics?

Big data analytics is a process of analyzing large amounts of data to identify patterns, trends, and insights that can be used to improve business operations and decision-making.

19. What are the benefits of using big data analytics for businesses?

The benefits of using big data analytics for businesses include improved operations, better insights into customer behavior, and cost savings.

20. What is a distributed file system?

A distributed file system is a file system that allows data to be stored in multiple computers or servers, providing a scalable and fault-tolerant way to store and manage large amounts of data.

21. What are the benefits of using a distributed file system?

The benefits of using a distributed file system include improved scalability, fault-tolerance, and cost savings by allowing data to be stored in commodity hardware instead of high-end servers.

22. What are the different formats of data?

The different formats of data include unstructured, semi-structured, and structure

23. How does HDFS address the issue of data storage in different formats?

HDFS creates an abstraction of resources, similar to virtualization, and allows for data replication across multiple systems to handle various types of data from various sources.

24. What is the master-slave architecture in HDFS?

The master-slave architecture in HDFS involves a name node as a master node and data nodes as slaves. The name node contains metadata about the data stored in the data nodes, such as which data block is stored in which data node, and where the replications of the data block are kept. The actual data is stored in the data nodes, and the replication factor is three by default.

25. How does Hadoop solve the problem of storing big data?

Hadoop provides a distributed way to store big data by dividing the data into blocks, storing it across different data nodes, and replicating the data blocks on them. This simplifies the process of storing large amounts of data, such as one TB of data, on multiple 128 G B systems or less.

26. What is the scaling problem?

The scaling problem refers to the challenge of storing and managing large amounts of data that cannot be handled by a single system.

27. How does HDFS address the scaling problem?

HDFS focuses on horizontal scaling instead of vertical scaling. By adding extra data nodes to the HDFS cluster when required, users can store more data nodes without increasing the resources of their data nodes. This simplifies the process of storing large amounts of data.

28. What are the three challenges of big data?

The three challenges of big data are storing a variety of data types, accessing data faster, and managing traffic congestion.

29. How does HDFS eliminate pre-dumping schema validation?

HDFS eliminates pre-dumping schema validation by allowing users to dump all data types in one place and read multiple models for insights.

30. What is the solution to accessing data faster in big data?

To solve the problem of accessing data faster in big data, we can move processing to data rather than data to processing. This means sending logic to slaves, which store the data and perform processing in them, resulting in smaller chunks of the result being sent to our name node, reducing network congestion and input output channel congestion.

31. What is the role of data engineers in the data-driven process?

They are responsible for building a pipeline for data collection and storage, funneling the data-to-data analysts and scientists.

32. What are the primary responsibilities of a big data engineer?

Handling the extract, transform, and load process, improving data foundational procedures, integrating new data management technologies and software into existing systems, building data collection pipelines, and performance tuning.

33. What is the goal of data transformation in data engineering?

Integrating and transforming data for a specific use case, with a major skill set being the knowledge of SQL.

34. What are some of the challenges in data ingestion for a data engineer?

Getting data out of source systems and ingesting it into a data lake, including multiple approaches for both batch and real-time extraction, incremental data loading, fitting within small source windows, and parallelization of data loading.

35. What is the purpose of interactive data processing test in big data testing?

To test the application when it is in interactive data processing mode.

36. What is de-normalization in data modeling?

The process of unavoidably duplicating data to improve query performance.

37. What is partitioning in data modeling?

The process of dividing data into smaller and more manageable units for querying and analysis.

38. What is indexing in data modeling?

The process of creating a reference structure to speed up data retrieval.

39. What is the difference between SQL and NoSQL databases?

SQL databases use structured query language (SQL) to manage and manipulate data, while NoSQL databases use non-structured query languages to handle large volumes of unstructured data.

40. What is the role of HDFS (Hadoop Distributed File System) in the Hadoop framework?

The acronym “HDFS” stands for Hadoop Distributed File System. HDFS is a distributed file system designed to work on commodity hardware. It is designed to run on low-cost commodity hardware and is fault-tolerant. HDFS was meant to be deployed.

41. What is the role of YARN (Yet Another Resource Negotiator) in the Hadoop framework?

It performs resource management by allocating resources to different applications and scheduling jobs.

42. What is MapReduce in the Hadoop framework?

It is a parallel processing paradigm that allows data to be processed parallelly on top of HDFS.

43. What is the role of Hive in the Hadoop framework?

It is a data warehousing tool on top of HDFS, catering to professionals from an SQL background for analytics.

44. What is the role of Apache Pig in the Hadoop framework?

It is a high-level platform used for data transformation on top of Hadoop.

45. What is the role of Flume and Scoop in the Hadoop framework?

Flume is used to import unstructured data to HDFS, and Scoop is used to import and export structured data from RDBMS.

46. What is real-time processing in data engineering?

The ability to process and analyze data in real-time, such as credit card fraud detection or recommendation systems.

47. What is Apache Spark in data engineering?

A distributed Real-time Processing Framework that can be easily integrated with Hadoop, leveraging HDFS.

48. What is the role of programming languages in data engineering?

Knowledge of one programming language is enough to serve the same purpose, but Python is an easy language to learn due to its syntax and good community support, while R has a steep learning curve, developed by statisticians and mostly used by analysts and data scientists.

49. What is the purpose of real time processing test in big data testing?

To ensure the application is tested in a real-time environment and checked for stability.

50. What is the purpose of data warehousing in business intelligence solutions?

To manage large amounts of data from various sources, which are used for analytics and reporting.

51. What are the different forms of big data?

Structured, semi-structured, and unstructured formats.

52. What is structured data?

Tabular data organized under rows and columns with easy accessibility.

53. What is semi-structured data?

Data between structured and unstructured data that cannot be directly ingested into an RDBMS due to metadata, tags, and sometimes duplicate values.

54. What is unstructured data?

Data that is difficult to store and retrieve and is generated by organizations like image files, video files, and audio files.

55. What is the big data testing environment?

It requires space for storing processing and validating terabytes of data, a responsive cluster and its nodes, and data processing resources such as a powerful CPU.

56. What is the general approach in big data testing?

It involves three stages: data ingestion, data processing, and validation.

57. What is functional testing in testing big data applications?

It deals with dealing with huge blocks of data, which can often bring data issues such as bad data, duplicate values, meta data, missing values, and more.

58. What is the purpose of report generation phase in functional testing of big data?

It deals with data validation form measures and dimensions, real-time reporting, data drill up and down mechanisms, and business reports and chats.

59. Whatis non-functional testing in big data systems?

It is a crucial phase in the development of big data systems, which involves five stages: data quality monitoring, infrastructure, data security, data performance, and failover testing mechanism.

Let’s be more sparkle by reading MCQ’S of Hadoop.

1) Which of the following is not an important skill for big data engineers?

1. Data warehousing

2. Operating systems

3. Testing big data

4. Programming

2) Which of the following forms of big data is not easily accessible?

1. Structured data

2. Semi-structured data

3. Unstructured data

4. Numerical data

3) In which stage of the big data testing environment is data cross-checked for errors and missing values?

1. Data ingestion

2. Data processing

3. Data validation

4. None of the above

4) Which of the following is a tool used for data ingestion in the big data testing environment?

1. Hive SQL

2. HDFS

3. MongoDB

4. All of the above

5) Which of the following skills is not essential for data engineers?

1. Mastering data warehousing and ETL tools

2. Knowledge of operating systems such as UNIX, Linux, and Solaris

3. Knowledge of statistical analysis, data modeling, and machine learning in cloud environments

4. Integrating data warehousing and ETL tools with big data frameworks

6) Which of the following is a reason for big data testing?

1. There may be many possibilities for failure

2. It is not necessary for big data applications

3. It is expensive to test big data applications

4. It is not feasible to test big data applications

7) Which of the following scenarios is not a main focus of big data testing?

1. Batch data processing test

2. Interactive data processing test

3. Real time data processing test

4. None of the above

8) Which of the following tools is not used in big data testing?

1. Hive SQL

2. HDFS

3. Spark

4. ClickView

9) Which of the following is not a technology that has contributed to the emergence of Big Data?

1. IoT devices

2. Mobile phones

3. Autonomous devices like robotics, drones, vehicles, and appliances

4. Cloud storage

10) Which of the following is not a statistic related to Big Data?

1. There are 2.5 quintillion bytes of data created every day

2. Big Data refers to a collection of data that is so huge and complex that none of the traditional data management tools can handle it

3. The volume of data generated by self-driving cars is not increasing exponentially

4. All of the above

Hadoop enables companies choose the right tools and technology because to its flexibility.

Distribution speeds up data processing, making Hadoop ideal for big data analytics and machine learning.

Modern data-driven companies require Hadoop’s strong and scalable technology to analyze massive volumes of data.

I hope you will rock in your next interview.

All the best!!!