Top 10 Hadoop Interview Questions with Answers for Freshers

Hadoop is an open-source framework that allows storing and processing of large amounts of data in a distributed computing environment. Organizations widely use it to handle big data and gain insights from it. Hadoop has become an essential tool for data analysts and data scientists. 

As a Hadoop Developer or Analyst, you can easily earn around INR 3 lakhs to INR 11 Lakhs annually, with an average salary of INR 5.5 lakhs. 

If you are a fresher looking for a job in the field of big data, you may be asked several Hadoop interview questions during the selection process. 

In this blog, we will discuss the top 10 Hadoop interview questions with their answers to help you prepare for your interview.

Top 10 Questions in Hadoop Interviews

• What is Hadoop?

Hadoop is an open-source software framework that allows distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale from single servers to thousands of machines, each offering local computation and storage. Hadoop provides a reliable, scalable, and fault-tolerant platform for handling big data.

• What are the key components of Hadoop?

The key components of Hadoop are:

• Hadoop Distributed File System (HDFS): It is the primary storage system used by Hadoop. It stores large files across multiple machines in a distributed manner.
• MapReduce is a programming model used for processing large datasets in parallel across a distributed cluster.
• YARN: It is the resource manager Hadoop uses to manage resources and schedule tasks in a cluster.
• Hadoop Common includes the common utilities and libraries used by other Hadoop modules.

• What is NameNode in Hadoop?

NameNode is a master node in Hadoop's HDFS architecture that manages the file system metadata and coordinates client data access requests. It maintains the directory tree of all files in the file system, and it tracks the location of each block of data. NameNode also decides which DataNode should receive data from a client, and it monitors the health of DataNodes.

• What is the role of DataNode in Hadoop?

DataNode is a worker node in Hadoop's HDFS architecture that stores data in a distributed manner. Each DataNode stores a portion of the data stored in HDFS. DataNodes communicate with the NameNode to report their health status and receive instructions for data replication and rebalancing.

• What are the different types of joins in Hadoop MapReduce?

Hadoop MapReduce supports three types of joins:

• Map-side Join: In this join, the join operation is performed before the Map phase. The data from both tables are pre-processed and then sent to the Mapper.
• Reduce-side Join: The join operation is performed after the Map phase, and the join key groups data before being sent to the reducer.
• Composite Join: Multiple MapReduce jobs perform the join operation in this join.
  

• What is the purpose of HBase in Hadoop?

HBase is a NoSQL database that runs on top of Hadoop. It provides random and real-time access to large datasets. HBase is used for storing and retrieving large amounts of structured data. It is scalable, fault-tolerant, and provides automatic sharding of tables. HBase is ideal for applications that require low-latency data access.

• How does Hadoop handle data security?

Hadoop provides data security through various mechanisms, such as:

• Authentication: It verifies the identity of users and allows them to access the data based on their privileges.
• Authorization: It specifies the user access level to the data.
• Data Encryption: It encrypts the data during transmission and at rest to prevent unauthorized access.
• Auditing: It keeps track of who accessed the data and when.
  

• What is the role of a Secondary NameNode in Hadoop?

A Secondary NameNode in Hadoop is a helper node that performs periodic checkpoints of the Hadoop file system metadata. It merges the edits log with the file system image to create a new snapshot of the file system metadata. This helps to prevent data loss and improve the performance of the NameNode.

• What are the different compression techniques supported by Hadoop?

Hadoop supports various compression techniques to reduce the size of data stored in HDFS. Some of the popular compression techniques are:

• Gzip is a widely used compression algorithm that provides good compression ratios.
• Snappy: It is a fast compression algorithm that provides moderate compression ratios.
• LZO is a compression algorithm that provides high compression ratios and fast decompression times. However, it requires a separate license.
  

• How does Hadoop handle data skew, and what techniques can be used to mitigate it?

Data skew is a common problem in Hadoop, where a few keys or partitions contain a disproportionate amount of data, leading to uneven workload distribution among nodes in the cluster. Hadoop offers several techniques to handle data skew, such as:

• Partitioning: Data can be partitioned based on a specific column or key to distribute the data across nodes evenly. Hadoop supports various partitioning techniques, such as range partitioning, hash partitioning, and composite partitioning.
• Combiners: Combiners combine the output of Map tasks before sending it to the Reduce task. This helps to reduce the amount of data that needs to be transferred over the network and reduces the workload on the reducer.
• Sampling: Sampling can identify skewed data by selecting and analysing a small subset of data and analyzing it. Based on the analysis, appropriate partitioning techniques can be applied.
• SkewTune: SkewTune is a Hadoop plugin that automatically detects and mitigates data skew by dynamically adjusting the partitioning scheme.

Conclusion

A Hadoop online workshop from Skill-Lync can help students prepare well for interviews by comprehensively understanding Hadoop concepts and practical experience with Hadoop tools and technologies. The workshop covers all the essential topics, including HDFS, MapReduce, Hive, and Pig, along with hands-on projects that enable students to apply their knowledge to real-world scenarios. 

Moreover, it includes mock interviews, practice exercises, and career guidance, which help students prepare for Hadoop interviews and stand out from other candidates. By enrolling in a computer science engineering online course from Skill-Lync, students can gain the skills and confidence they need to ace Hadoop interviews and secure their dream jobs. You can also take a demo class with us to learn about it better.