Hadoop Developer Interview Questions & Answers Which You Should Not Miss!

Big Data Hadoop Training Course is designed to prepare you for your next project in the world of Big Data. Hadoop is the industry leader among Big Data Technologies and it is a principal skill for every expert in this field. Spark is also gaining connotation with emphasis on real-time processing. As a big data professional these are mandatory skills.

1. What are real-time industry purposes of Hadoop?
Hadoop, familiar as Apache Hadoop, is a free software platform for scalable and distributed computing of big volumes of data. It provides quick, high performance and gainful analysis of structured and unstructured data generated on digital platforms and within the activity. It is used in roughly all departments and sectors today.

2. How is Hadoop diverse from other parallel computing systems?
Hadoop is a distributed file system, which lets you accumulate and handle the enormous amount of data on a cloud of machines, handling data redundancy. The main benefit is that since data is saved in several nodes, it is superior to process it in distributed mode.

3. Which modes Hadoop can be run in?
Hadoop can run in three modes:
a. Standalone Mode: . This mode is chiefly used for debugging purpose, and it does not support the use of HDFS.
b. Pseudo-Distributed Mode (Single Node Cluster):In this case, you need configuration for all the three files described above.
c. Fully-Distributed Mode (Multiple Cluster Node): This is the production stage of where data is utilized and dispersed across several nodes on a Hadoop cluster.

4. Explain the major distinction between HDFS block and InputSplit.
In simple terms, the block is the physical symbol of data while split is the logical representation of data present in the block. Split acts as a mediator between block and mapper

5. What is distributed cache and what are its advantages?
Distributed Cache, in Hadoop, is a service by MapReduce framework to cache files when needed.

6. Explain the disparity between NameNode, Checkpoint NameNode, and BackupNode.
NameNode is the core of HDFS that handles the metadata – the information of what file maps to what block locations and what blocks are saved on what datanode.

Checkpoint NameNode has the similar directory structure as NameNode and creates checkpoints for a namespace at regular periods by downloading the fsimage and edits the file and margining them within the local directory.

Backup Node offers similar functionality as Checkpoint, implementing harmonization with NameNode. It maintains an up-to-date in-memory copy of file system namespace and doesn’t necessitate getting hold of changes after regular phases.

7. What are the most frequent Input layouts in Hadoop?
There are three most regular key in formats in Hadoop:
• Text Input layout: Default input format in Hadoop.
• Key Value Input design: used for plain text files where the files are split into lines
• Sequence File Input Format: used for reading files in succession

8. Classify DataNode and how does NameNode tackle DataNode failures?
DataNode stores data in HDFS; it is a node where actual data exists in the file system. If the namenode does not obtain a message from datanode. The NameNode manages the duplication of data blocks from one DataNode to other.

9. What are the chief approaches of a Reducer?
The three chief approaches of a Reducer are:
1. setup(): this approach is used for configuring different parameters like input data size, distributed cache.
2. reduce(): spirit of the reducer always called once per key with the connected reduced task
public void reduce(Key, Value, context)
3. cleanup(): this procedure is called to clean temporary files, only once at the conclusion of the task
public void cleanup (context)

10.What is SequenceFile in Hadoop?
Broadly used in MapReduce I/O formats, SequenceFile is a flat file containing binary key/value pairs.